diff --git a/README.md b/README.md
index c238a8f..6b9a9f8 100644
--- a/README.md
+++ b/README.md
@@ -70,3 +70,8 @@ run it (torch)
 
 NOTES:
 If you're using using the torch parts only, you can `export XLA_PYTHON_CLIENT_PREALLOCATE=false` to prevent jax from doing jax things and hogging your VRAM
+For rapid iteration, `jax.jit` might be too slow. In this case, set:
+```
+JAX_DISABLE_JIT=True
+```
+in your environment to disable it.
diff --git a/download_weights.py b/download_weights.py
index b8d19d3..fc472ec 100644
--- a/download_weights.py
+++ b/download_weights.py
@@ -51,6 +51,7 @@ def translate_key(in_key: str):
 
 
 def reverse_permute(tensor: torch.Tensor, n_heads: int = 32, dim1:int = 4096, dim2: int = 4096) -> torch.Tensor:
+# def reverse_permute(tensor: torch.Tensor, n_heads: int = 24, dim1:int = 3072, dim2: int = 3072) -> torch.Tensor:
     return tensor.view(n_heads, 2, dim1 // n_heads // 2, dim2).transpose(1, 2).reshape(dim1, dim2)
 
 
@@ -88,9 +89,9 @@ def main(model_id: str, out_dir: Path):
             name = translate_key(hf_name)
             if name.endswith('wq.weight'):
                 param = reverse_permute(param, n_heads=32, dim1=2048, dim2=2048)  # 1B
-                #param = reverse_permute(param, n_heads=24, dim1=3072, dim2=3072)  # 3B
+                # param = reverse_permute(param, n_heads=24, dim1=3072, dim2=3072)  # 3B
                 #param = reverse_permute(param, n_heads=32, dim1=4096, dim2=4096)  # 7B
-                #param = reverse_permute(param, n_heads=64, dim1=8192, dim2=8192)   # 70B
+                # param = reverse_permute(param, n_heads=64, dim1=8192, dim2=8192)   # 70B
                 #param = reverse_permute(param, n_heads=96, dim1=12288, dim2=12288)   # 123B
                 #param = reverse_permute(param, n_heads=128, dim1=16384, dim2=16384) # 405B
                 #param = reverse_permute(param, n_heads=128, dim1=12288, dim2=12288) # DSV2
@@ -98,9 +99,9 @@ def main(model_id: str, out_dir: Path):
                 #param = reverse_permute(param, n_heads=48, dim1=6144, dim2=6144)    # Mixtral8x22B
             elif name.endswith('wk.weight'): #wk.weight
                 param = reverse_permute(param, n_heads=8, dim1=512, dim2=2048)  # 1B
-                #param = reverse_permute(param, n_heads=8, dim1=1024, dim2=3072)  # 3B
+                # param = reverse_permute(param, n_heads=8, dim1=1024, dim2=3072)  # 3B
                 #param = reverse_permute(param, n_heads=8, dim1=1024, dim2=4096)  # 7B
-                #param = reverse_permute(param, n_heads=8, dim1=1024, dim2=8192)   # 70B
+                # param = reverse_permute(param, n_heads=8, dim1=1024, dim2=8192)   # 70B
                 #param = reverse_permute(param, n_heads=8, dim1=1024, dim2=12288)   # 123B
                 #param = reverse_permute(param, n_heads=8, dim1=1024, dim2=16384)  # 405B
                 #param = reverse_permute(param, n_heads=128, dim1=12288, dim2=12288)  # DSV2
@@ -115,4 +116,4 @@ def main(model_id: str, out_dir: Path):
 
 
 if __name__ == "__main__":
-    tyro.cli(main)
\ No newline at end of file
+    tyro.cli(main)
diff --git a/entropix/config.py b/entropix/config.py
index b1ab03b..84560c4 100644
--- a/entropix/config.py
+++ b/entropix/config.py
@@ -15,6 +15,34 @@
   "max_seq_len": 4096
 }
 
+# params = {
+#   "dim": 8192,
+#   "n_layers": 80,
+#   "n_heads": 64,
+#   "n_kv_heads": 8,
+#   "vocab_size": 128256,
+#   "ffn_dim_multiplier": 1.5,
+#   "multiple_of": 256,
+#   "norm_eps": 1e-05,
+#   "rope_theta": 500000.0,
+#   "use_scaled_rope": True,
+#   "max_seq_len": 4096
+# }
+
+# LLama 3B
+params_3b = {
+  "dim": 3072,
+  "n_layers": 28,
+  "n_heads": 24,
+  "n_kv_heads": 8,
+  "vocab_size": 128256,
+  "ffn_dim_multiplier": 1.0,
+  "multiple_of": 256,
+  "norm_eps": 1e-05,
+  "rope_theta": 500000.0,
+  "use_scaled_rope": True,
+  "max_seq_len": 4096
+}
 
 class ModelParams(NamedTuple):
   n_layers: int
@@ -35,3 +63,13 @@ class ModelParams(NamedTuple):
   rope_theta=params["rope_theta"],
   use_scaled_rope=params["use_scaled_rope"]
 )
+
+LLAMA_3B_PARAMS = ModelParams(
+  n_layers=params_3b["n_layers"],
+  n_local_heads=params_3b["n_heads"],
+  n_local_kv_heads=params_3b["n_kv_heads"],
+  head_dim=params_3b["dim"] // params_3b["n_heads"],
+  max_seq_len=params_3b["max_seq_len"],
+  rope_theta=params_3b["rope_theta"],
+  use_scaled_rope=params_3b["use_scaled_rope"]
+)
diff --git a/entropix/data/prompts.csv b/entropix/data/acp.csv
similarity index 100%
rename from entropix/data/prompts.csv
rename to entropix/data/acp.csv
diff --git a/entropix/eval_main.py b/entropix/eval_main.py
new file mode 100644
index 0000000..512fb21
--- /dev/null
+++ b/entropix/eval_main.py
@@ -0,0 +1,274 @@
+from typing import List, Tuple, Dict
+
+import json
+import math
+from pathlib import Path
+
+import jax
+import jax.numpy as jnp
+import tyro
+
+import lm_eval
+from lm_eval import simple_evaluate
+from lm_eval.evaluator_utils import TaskOutput
+from lm_eval.api.model import LM
+from lm_eval.api.task import Task
+from lm_eval.utils import make_table
+
+from transformers import AutoTokenizer
+
+from entropix.config import LLAMA_1B_PARAMS, LLAMA_3B_PARAMS
+from entropix.kvcache import KVCache
+from entropix.model import xfmr
+from entropix.sampler import SamplerConfig, _sample, sample
+from entropix.prompts import create_prompts_from_csv, prompt
+from entropix.sampler import sample
+from entropix.tokenizer import Tokenizer
+from entropix.weights import load_weights
+
+DEFAULT_WEIGHTS_PATH = Path(__file__).parent / '../weights'
+
+def apply_scaling(freqs: jax.Array):
+  SCALE_FACTOR = 8
+  LOW_FREQ_FACTOR = 1
+  HIGH_FREQ_FACTOR = 4
+  OLD_CONTEXT_LEN = 8192  # original llama3 length
+
+  low_freq_wavelen = OLD_CONTEXT_LEN / LOW_FREQ_FACTOR
+  high_freq_wavelen = OLD_CONTEXT_LEN / HIGH_FREQ_FACTOR
+
+  def scale_freq(freq):
+    wavelen = 2 * math.pi / freq
+
+    def scale_mid(_):
+      smooth = (OLD_CONTEXT_LEN / wavelen - LOW_FREQ_FACTOR) / (HIGH_FREQ_FACTOR - LOW_FREQ_FACTOR)
+      return (1 - smooth) * freq / SCALE_FACTOR + smooth * freq
+
+    return jax.lax.cond(
+      wavelen < high_freq_wavelen,
+      lambda _: freq,
+      lambda _: jax.lax.cond(wavelen > low_freq_wavelen, lambda _: freq / SCALE_FACTOR, scale_mid, None),
+      None
+    )
+
+  return jax.vmap(scale_freq)(freqs)
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 500000.0, use_scaled: bool = False, dtype: jnp.dtype = jnp.float32) -> jax.Array:
+  freqs = 1.0 / (theta ** (jnp.arange(0, dim, 2)[: (dim // 2)].astype(dtype) / dim))
+  if use_scaled:
+    freqs = apply_scaling(freqs)
+  t = jnp.arange(end, dtype=dtype)
+  freqs = jnp.outer(t, freqs)
+  return jnp.exp(1j * freqs)
+
+
+def build_attn_mask(seqlen: int, start_pos: int) -> jax.Array:
+  mask = jnp.zeros((seqlen, seqlen), dtype=jnp.float32)
+  if seqlen > 1:
+    mask = jnp.full((seqlen, seqlen), float('-inf'))
+    mask = jnp.triu(mask, k=1)
+    mask = jnp.hstack([jnp.zeros((seqlen, start_pos)), mask], dtype=jnp.float32)
+  return mask
+
+class CustomLLaMAModel(LM):
+    def __init__(self, weights_path: Path):
+        super().__init__()
+        # self.model_params = LLAMA_1B_PARAMS
+        self.model_params = LLAMA_3B_PARAMS
+        self.xfmr_weights = load_weights(weights_path.absolute())
+        self.tokenizer = Tokenizer("entropix/tokenizer.model")
+        self._tokenizer_name = "meta-llama/Llama-3.2-1B-Instruct"
+        self.hf_tokenizer = AutoTokenizer.from_pretrained(self._tokenizer_name)
+        self.freqs_cis = precompute_freqs_cis(
+            self.model_params.head_dim,
+            self.model_params.max_seq_len,
+            self.model_params.rope_theta,
+            self.model_params.use_scaled_rope
+        )
+        self.sampler_cfg = SamplerConfig()
+
+    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+        """
+        Method to apply a chat template to a list of chat history between user and model.
+        """
+        return self.hf_tokenizer.apply_chat_template(
+            chat_history, tokenize=False, add_generation_prompt=True
+        )
+
+    def _model_generate(self, context_tokens: List[int], max_tokens: int):
+        gen_tokens = None
+        cur_pos = 0
+        tokens = jnp.array([context_tokens], jnp.int32)
+        bsz, seqlen = tokens.shape
+        attn_mask = build_attn_mask(seqlen, cur_pos)
+        kvcache = KVCache.new(self.model_params.n_layers, bsz, self.model_params.max_seq_len, self.model_params.n_local_kv_heads, self.model_params.head_dim)
+
+        logits, kvcache, _, _ = xfmr(self.xfmr_weights, self.model_params, tokens, cur_pos, self.freqs_cis[:seqlen], kvcache, attn_mask=attn_mask)
+        next_token = jnp.argmax(logits[:, -1], axis=-1, keepdims=True).astype(jnp.int32)
+        gen_tokens = next_token
+
+        cur_pos = seqlen
+        stop = jnp.array([128001, 128008, 128009])
+        # stop = jnp.array([128001, 128008, 128009] + stop_tokens)
+
+        while cur_pos < min(self.model_params.max_seq_len, len(context_tokens) + max_tokens):
+            cur_pos += 1
+            logits, kvcache, scores, _ = xfmr(self.xfmr_weights, self.model_params, next_token, cur_pos, self.freqs_cis[cur_pos:cur_pos+1], kvcache)
+            # next_token, _ = sample(gen_tokens, logits, scores, cfg=self.sampler_cfg)
+            next_token = sample(logits, scores, cfg=self.sampler_cfg)
+            gen_tokens = jnp.concatenate((gen_tokens, next_token))
+            if jnp.isin(next_token, stop).any():
+                break
+
+        return gen_tokens, logits
+
+    def generate_until(self, requests) -> List[str]:
+        res = []
+        # for request in requests:
+        for i, request in enumerate(requests):
+            context = request.args[0]
+            ## e.g. ['<|eot_id|>', '<|start_header_id|>user<|end_header_id|>', 'Q:', '</s>', '<|im_end|>']
+            stop_seqs = request.args[1]["until"]
+            ## e.g. [[128009], [128006, 882, 128007], [48, 25], [524, 82, 29], [27, 91, 318, 6345, 91, 29]]
+            ## some stop_seqs are comprised of multiple tokens
+            ## lm-eval uses multi token stop criteria, 
+            ## https://github.com/rasdani/lm-evaluation-harness/blob/9b052fdccae265d6cc422f463136d2da7c2541b2/lm_eval/models/utils.py#L254
+            # stop_tokens = [self.tokenizer.encode(stop_seq, bos=False, eos=False, allowed_special='all') for stop_seq in stop_seqs]
+            context_tokens = self.tokenizer.encode(context, bos=False, eos=False, allowed_special='all')
+            generated_tokens, _ = self._model_generate(context_tokens, self.max_gen_toks)
+            _res = []
+            # for t in generated_tokens:
+            #     _decoded = self.tokenizer.decode(t)
+            #     for stop_seq in stop_seqs:
+            #         if stop_seq in _decoded:
+            #             pass
+            #             stop_index = _decoded.index(stop_seq)
+            #             _decoded = _decoded[:stop_index]
+            #             break
+            #     _res.append(_decoded)
+            # decoded = ''.join(_res)
+            generated_tokens = [t.item() for t in generated_tokens]
+            decoded = self.tokenizer.decode(generated_tokens)
+            print(f"Sample Nr. {i}")
+            print(decoded)
+            res.append(decoded)
+
+        print(res)
+        return res
+
+    def loglikelihood(self, requests):
+      res = []
+      for request in requests:
+          context = request.args[0]
+          continuation = request.args[1]
+
+          context_tokens = self.tokenizer.encode(context, bos=False, eos=False, allowed_special='all')
+          continuation_tokens = self.tokenizer.encode(continuation, bos=False, eos=False, allowed_special='all')
+          all_tokens = context_tokens + continuation_tokens
+
+          tokens, logits = self._model_generate(context_tokens, len(continuation_tokens))
+
+          continuation_logprobs = jax.nn.log_softmax(jnp.array(logits), axis=-1)
+          greedy_tokens = jnp.argmax(continuation_logprobs, axis=-1)
+          max_equal = jnp.all(greedy_tokens == jnp.array(continuation_tokens))
+
+          selected_logprobs = continuation_logprobs[jnp.arange(len(continuation_tokens)), jnp.array(continuation_tokens)]
+          logprob_sum = jnp.sum(selected_logprobs)
+
+          res.append((float(logprob_sum), bool(max_equal)))
+      return res
+
+
+    def loglikelihood_rolling(self, requests):
+        res = []
+        for request in requests:
+            context = request.args[0]
+            continuation = request.args[1]
+
+            context_tokens = self.tokenizer.encode(context, bos=False, eos=False, allowed_special='all')
+            continuation_tokens = self.tokenizer.encode(continuation, bos=False, eos=False, allowed_special='all')
+            all_tokens = context_tokens + continuation_tokens
+
+            tokens, logits = self._model_generate(context_tokens, len(continuation_tokens))
+
+            token_logprobs = jax.nn.log_softmax(jnp.array(logits), axis=-1)
+            selected_logprobs = token_logprobs[jnp.arange(len(continuation_tokens)), jnp.array(continuation_tokens)]
+
+            res.append(selected_logprobs.tolist())
+
+        return res
+
+    @property
+    def eot_token_id(self) -> int:
+        return self.tokenizer.eos_id()
+
+    @property
+    def max_length(self) -> int:
+        return self.model_params.max_seq_len
+
+    @property
+    def max_gen_toks(self) -> int:
+        return 256
+
+    @property
+    def batch_size(self) -> int:
+        return 12  # The original implementation doesn't use batching
+
+    @property
+    def device(self) -> str:
+        return 'cuda'  # Adjust if using GPU/TPU
+
+    @property
+    def tokenizer_name(self) -> str:
+        return self._tokenizer_name
+
+    @tokenizer_name.setter
+    def tokenizer_name(self, value: str):
+        self._tokenizer_name = value
+
+def main(
+    # weights_path: Path = DEFAULT_WEIGHTS_PATH.joinpath("1B-Instruct"),
+    weights_path: Path = DEFAULT_WEIGHTS_PATH.joinpath("3B-Instruct"),
+    tasks: List[str] = ["gsm8k_cot_llama"],
+    # tasks: List[str] = ["mmlu"],
+    # tasks: List[str] = ["mmlu_stem", "mmlu_other", "mmlu_social_sciences", "mmlu_humanities"],
+    # tasks: List[str] = ["mmlu_stem_tasks", "mmlu_other_tasks", "mmlu_social_sciences_tasks", "mmlu_humanities_tasks"],
+    # tasks: List[str] = ["mmlu_stem_tasks"],
+    # tasks: List[str] = ["arc_easy"],
+):
+
+    tasks_dict: Dict[str, Task] = lm_eval.tasks.get_task_dict(tasks)
+    tasks_list: List[Task] = list(tasks_dict.values())
+
+    model = CustomLLaMAModel(weights_path)
+
+    results = simple_evaluate(
+        model=model,
+        tasks=tasks_list,
+        # limit=1,
+        # limit=2,
+        # limit=5,
+        # limit=10,
+        limit=30,
+        apply_chat_template=True,
+        fewshot_as_multiturn=True,
+    )
+
+
+    # Print the results in a formatted way
+    print(make_table(results))
+
+    # If you want to save the results to a file/
+    # output.save_json("leaderboard.json")
+    with open("results.json", "w") as f:
+        json.dump(results, f)
+
+if __name__ == '__main__':
+    import datetime
+    start_time = datetime.datetime.now() 
+    tyro.cli(main)
+    current_time = datetime.datetime.now()
+    print(f"Start time: {start_time}")
+    print(f"Current time: {current_time}")
+    print(f"Total time taken: {current_time - start_time}")
diff --git a/entropix/kvcache.py b/entropix/kvcache.py
index 392c80a..c5571d7 100644
--- a/entropix/kvcache.py
+++ b/entropix/kvcache.py
@@ -18,12 +18,8 @@ def new(cls, layers: int, bsz: int, max_seq_len: int, kv_heads: int, head_dim: i
   def update(self, xk: jax.Array, xv: jax.Array, layer_idx: int, cur_pos: int, n_rep: int):
     ck = jax.lax.dynamic_update_slice(self.k, jnp.bfloat16(xk[None, ...]), (layer_idx, 0, cur_pos, 0, 0))
     cv = jax.lax.dynamic_update_slice(self.v, jnp.bfloat16(xv[None, ...]), (layer_idx, 0, cur_pos, 0, 0))
-    if cur_pos == 0:
-      keys = jnp.repeat(xk, n_rep, axis=2)
-      values = jnp.repeat(xv, n_rep, axis=2)
-    else:
-      keys = jnp.repeat(ck[layer_idx], n_rep, axis=2)
-      values = jnp.repeat(cv[layer_idx], n_rep, axis=2)
+    keys = jnp.repeat(ck[layer_idx], n_rep, axis=2)
+    values = jnp.repeat(cv[layer_idx], n_rep, axis=2)
 
     return keys, values, KVCache(k=ck, v=cv)
 
diff --git a/entropix/main.py b/entropix/main.py
index b8dcc42..fb6c826 100644
--- a/entropix/main.py
+++ b/entropix/main.py
@@ -1,3 +1,5 @@
+from typing import Tuple
+
 import math
 from pathlib import Path
 
@@ -9,10 +11,11 @@
 from entropix.kvcache import KVCache
 from entropix.model import xfmr
 from entropix.sampler import SamplerConfig, sample
-from entropix.prompts import create_prompts_from_csv, prompt
+from entropix.prompts import create_prompts_from_csv, prompt6, p4o
 from entropix.sampler import sample
 from entropix.tokenizer import Tokenizer
 from entropix.weights import load_weights
+from entropix.config import LLAMA_1B_PARAMS, LLAMA_3B_PARAMS
 
 DEFAULT_WEIGHTS_PATH = Path(__file__).parent / '../weights'
 
@@ -59,11 +62,15 @@ def build_attn_mask(seqlen: int, start_pos: int) -> jax.Array:
     mask = jnp.hstack([jnp.zeros((seqlen, start_pos)), mask], dtype=jnp.float32)
   return mask
 
-
-def main(weights_path: Path = DEFAULT_WEIGHTS_PATH.joinpath('1B-Instruct')):
-  model_params = LLAMA_1B_PARAMS
-  xfmr_weights = load_weights(weights_path.absolute())
+# def main(weights_path: Path = DEFAULT_WEIGHTS_PATH.joinpath('1B-Instruct')):
+def main(weights_path: Path = DEFAULT_WEIGHTS_PATH.joinpath('3B-Instruct')):
+# def main(weights_path: Path = DEFAULT_WEIGHTS_PATH.joinpath('70B-Nemotron-Instruct')):
+  # model_params = LLAMA_1B_PARAMS
+  model_params = LLAMA_3B_PARAMS
+  xfmr_weights = load_weights(weights_path.absolute(), n_layers=model_params.n_layers)
   tokenizer = Tokenizer('entropix/tokenizer.model')
+  xfmr_fn = jax.jit(xfmr, static_argnames=("model_params",))
+  sample_fn = jax.jit(sample, static_argnames=("cfg",))
 
   # Create the batch of tokens
   def generate(xfmr_weights, model_params, tokens):
@@ -74,35 +81,46 @@ def generate(xfmr_weights, model_params, tokens):
     attn_mask = build_attn_mask(seqlen, cur_pos)
     freqs_cis = precompute_freqs_cis(model_params.head_dim, model_params.max_seq_len, model_params.rope_theta, model_params.use_scaled_rope)
     kvcache = KVCache.new(model_params.n_layers, bsz, model_params.max_seq_len, model_params.n_local_kv_heads, model_params.head_dim)
-    logits, kvcache, _, _ = xfmr(xfmr_weights, model_params, tokens, cur_pos, freqs_cis[:seqlen], kvcache, attn_mask=attn_mask)
+    logits, kvcache, _, _ = xfmr_fn(xfmr_weights, model_params, tokens, cur_pos, freqs_cis[:seqlen], kvcache, attn_mask=attn_mask)
     next_token = jnp.argmax(logits[:, -1], axis=-1, keepdims=True).astype(jnp.int32)
-    gen_tokens = next_token
     print(tokenizer.decode([next_token.item()]), end='', flush=True)
     cur_pos = seqlen
     stop = jnp.array([128001, 128008, 128009])
     sampler_cfg = SamplerConfig()
+    gen_tokens = [next_token]
     while cur_pos < 8192:
       cur_pos += 1
-      logits, kvcache, scores, stats = xfmr(xfmr_weights, model_params, next_token, cur_pos, freqs_cis[cur_pos:cur_pos+1], kvcache)
-      next_token = sample(gen_tokens, logits, scores, cfg=sampler_cfg)
-      gen_tokens = jnp.concatenate((gen_tokens, next_token))
-      print(tokenizer.decode(next_token.tolist()[0]), end='', flush=True)
+      logits, kvcache, scores, stats = xfmr_fn(xfmr_weights, model_params, next_token, cur_pos, freqs_cis[cur_pos:cur_pos+1], kvcache)
+      next_token = sample(logits, scores, cfg=sampler_cfg)
+      gen_tokens.append(next_token)
+      out_token = tokenizer.decode(next_token.tolist()[0])
+      print(out_token, end='', flush=True)
       if jnp.isin(next_token, stop).any():
         break
 
-  csv_path = Path('entropix/data/prompts.csv')
-  prompts = create_prompts_from_csv(csv_path)
-  PROMPT_TEST = False
-
-  if PROMPT_TEST:
-    for p in prompts:
-      print(p)
-      tokens = tokenizer.encode(p,  bos=False, eos=False, allowed_special='all')
-      generate(xfmr_weights, model_params, tokens)
-  else:
-    print(prompt)
-    tokens = tokenizer.encode(prompt,  bos=False, eos=False, allowed_special='all')
-    generate(xfmr_weights, model_params, tokens)
+  prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+Cutting Knowledge Date: December 2023
+Today Date: 23 July 2024
+
+You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>
+ 
+Think carefully in a step-by-step manner. Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+"""
+  print(prompt)
+  tokens = tokenizer.encode(prompt,  bos=False, eos=False, allowed_special='all')
+  generate(xfmr_weights, model_params, tokens)
+
+
+import os
+os.environ['XLA_FLAGS'] = (
+    '--xla_gpu_enable_triton_softmax_fusion=true '
+    '--xla_gpu_triton_gemm_any=True '
+    '--xla_gpu_enable_async_collectives=true '
+    '--xla_gpu_enable_latency_hiding_scheduler=true '
+    '--xla_gpu_enable_highest_priority_async_stream=true '
+)
 
 if __name__ == '__main__':
   tyro.cli(main)
diff --git a/entropix/model.py b/entropix/model.py
index 31acb91..52ad4ad 100644
--- a/entropix/model.py
+++ b/entropix/model.py
@@ -14,12 +14,9 @@
 DEFAULT_MASK_VALUE = -0.7 * float(jnp.finfo(jnp.dtype("float32")).max)
 
 
-#@partial(jax.jit, static_argnames=("eps"))
 def rms_norm(x: jax.Array, w: jax.Array, eps: float = 1e-6) -> jax.Array:
   return w * (x * jax.lax.rsqrt(jax.lax.pow(x, 2).mean(-1, keepdims=True) + eps))
 
-
-#@partial(jax.jit, static_argnames=("dtype"))
 def apply_rotary_emb(xq: jax.Array, xk: jax.Array, freqs_cis: jax.Array, dtype: jnp.dtype = jnp.float32) -> Tuple[jax.Array, jax.Array]:
   reshape_xq = xq.astype(jnp.float32).reshape(*xq.shape[:-1], -1, 2)
   reshape_xk = xk.astype(jnp.float32).reshape(*xk.shape[:-1], -1, 2)
@@ -31,7 +28,6 @@ def apply_rotary_emb(xq: jax.Array, xk: jax.Array, freqs_cis: jax.Array, dtype:
   xk_out = jnp.stack((jnp.real(xk_out), jnp.imag(xk_out)), axis=-1).reshape(*xk_out.shape[:-1], -1)
   return xq_out.astype(dtype), xk_out.astype(dtype)
 
-#@partial(jax.jit, static_argnames=("model_params", "cur_pos", "layer_idx"))
 def attention(x: jax.Array, layer_weights: LayerWeights, model_params, cur_pos: int, layer_idx: int, freqs_cis: jax.Array, kvcache: KVCache, attn_mask: Optional[jax.Array] = None) -> Tuple[jax.Array, KVCache]:
   bsz, _, _ = x.shape
   n_rep = model_params.n_local_heads // model_params.n_local_kv_heads
@@ -46,8 +42,8 @@ def attention(x: jax.Array, layer_weights: LayerWeights, model_params, cur_pos:
   scores = jnp.matmul(xq, keys)
   pre_scores = scores / jnp.sqrt(model_params.head_dim)
   scores = pre_scores.astype(jnp.float32)  # Always do attention softmax at float32
-  if cur_pos == 0:
-    scores = scores + attn_mask
+  if attn_mask is not None:
+    scores = scores.at[..., :attn_mask.shape[-1]].add(attn_mask)
   mask = jnp.where(scores != 0.0, scores, DEFAULT_MASK_VALUE)
   padded_logits = jnp.where((mask >= DEFAULT_MASK_VALUE * 0.5), scores, DEFAULT_MASK_VALUE)
   scores = jax.nn.softmax(padded_logits, axis=-1).astype(x.dtype)
@@ -56,11 +52,9 @@ def attention(x: jax.Array, layer_weights: LayerWeights, model_params, cur_pos:
   out = jnp.dot(output, layer_weights.wo.T)
   return out, kvcache, pre_scores
 
-#@partial(jax.jit)
 def feed_forward(x: jax.Array, layer_weights: LayerWeights) -> jax.Array:
  return jnp.dot(jax.nn.silu(jnp.dot(x, layer_weights.w1.T)) * jnp.dot(x, layer_weights.w3.T), layer_weights.w2.T)
 
-#@partial(jax.jit, static_argnames=("model_params", "cur_pos"))
 def xfmr(xfmr_weights: XfmrWeights, model_params: ModelParams, tokens: jax.Array, cur_pos: int, freqs_cis: jax.Array, kvcache: KVCache, attn_mask: Optional[jax.Array]=None) -> Tuple[jax.Array, KVCache]:
   h = xfmr_weights.tok_embeddings[tokens]
   attn_stats = AttnStats.new(
diff --git a/entropix/prompts.py b/entropix/prompts.py
index 8e40bc7..d90ff23 100644
--- a/entropix/prompts.py
+++ b/entropix/prompts.py
@@ -153,13 +153,23 @@ def create_prompts_from_csv(csv_path: str) -> List[str]:
 prompt4 = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
 You are a masterful story teller. you can paint with all the colors of the wind.<|eot_id|><|start_header_id|>user<|end_header_id|>
 
-Tell me a long and wonderful story aboout the adventures of the elven mage frieren and her band of heros<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+Tell me a long and wonderful story about the adventures of the elven mage frieren and her band of heros<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 """
 
 bp4 = """
 You are a masterful story teller. you can paint with all the colors of the wind.<|eot_id|>
 
-Let me tell you a story aboout the adventures of the elven mage frieren and her band of heros
+Let me tell you a story about the adventures of the elven mage frieren and her band of heros
+"""
+
+p4o = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+You are a master weaver of tales, a virtuoso of words painting vivid pictures that ignite the imagination. Your stories shimmer with an otherworldly enchantment, breathing life into realms uncharted and characters unforgettable. With each carefully crafted phrase, you beckon readers to embark on a journey through the portals of wonder you conjure, to lose themselves in the tapestries of adventure, magic, and emotion you spin.
+Now, prepare to summon the depths of your storytelling prowess. Let your words flow like an ancient elven incantation, each one pulsing with arcane power. Transport us to a realm where elven mages and valiant heroes ride forth on quests of legend, their paths intertwined by the threads of destiny. Unleash the full spectrum of your narrative sorcery to immerse us in this saga - make us feel the crackle of Frieren's magic dancing at her fingertips, smell the earthy musk of the forests they traverse, hear the clashing of swords and the whispering of ancient secrets on the wind. Weave for us an epic to stir the soul and haunt the memory long after the final word fades away.
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+Spin an enchanting tale recounting the epic adventures of Frieren, the elven mage of unparalleled skill, and her intrepid band of heroes. Let your words conjure the wonders and perils they face as they navigate lands steeped in magic and legend on a quest that will echo through the ages. Immerse us in this saga with the full force of your storytelling sorcery.
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+<|reserved_special_token_12|>
 """
 
 
diff --git a/entropix/sampler.py b/entropix/sampler.py
index e9b9420..e42f3e8 100644
--- a/entropix/sampler.py
+++ b/entropix/sampler.py
@@ -1,12 +1,13 @@
 from typing import Dict, Tuple
 
+import math
 import chex
 import jax
 import jax.numpy as jnp
 
 LN_2 = 0.69314718056  # ln(2) = 1.0 / LOG2_E
 
-@jax.jit
+
 def calculate_varentropy_logsoftmax(logits: jnp.ndarray, axis: int = -1) -> Tuple[jnp.ndarray, jnp.ndarray]:
     """Calculate the entropy and varentropy of the probability distribution using logsoftmax."""
     log_probs = jax.nn.log_softmax(logits, axis=axis)
@@ -47,14 +48,11 @@ def _sample( logits: jax.Array, *, temperature: float | jax.Array, top_p: float
 
 def calculate_metrics(logits: jnp.ndarray, attention_scores: jnp.ndarray) -> Dict[str, jnp.ndarray]:
     entropy, varentropy = calculate_varentropy_logsoftmax(logits)
-
     attention_probs = jax.nn.softmax(attention_scores, axis=-1)
     attn_entropy = -jnp.sum(attention_probs * jnp.log2(jnp.clip(attention_probs, 1e-10, 1.0)), axis=-1)
     attn_varentropy = jnp.var(attn_entropy, axis=1)
-
     mean_attention = jnp.mean(attention_probs, axis=1)
     agreement = jnp.mean(jnp.abs(attention_probs - mean_attention[:, None, :]), axis=(1, 2))
-
     interaction_strength = jnp.mean(jnp.abs(attention_scores), axis=(1, 2, 3))
 
     return {
@@ -68,54 +66,71 @@ def calculate_metrics(logits: jnp.ndarray, attention_scores: jnp.ndarray) -> Dic
 
 @chex.dataclass(kw_only=True, frozen=True)
 class SamplerConfig:
-    """
-    Encapsulation of all available sampler hyperparameters.
-
-    This should be a good starting point for baselining experiments.
-    """
-
-    temp: float = 0.666
+    # Sampling Hyperparameters
+    temperature: float = 0.666
     top_p: float = 0.90
     top_k: int = 27
-    min_p: float = 0.03  # Turn this down to 0.01 to reduce the shoggoth
-
-    low_ent_thresh: float = 0.1
-    low_vent_thresh: float = 0.1
-    med_ent_thresh: float = 3.0
-    high_ent_thresh: float = 5.0
-    high_vent_thresh: float = 5.0
-
-    # TODO this is a bit of a nasty mess, but also makes all the hyperparameters visible
-    helv_attn_ent_offset: float = 1.3
-    helv_attn_ent_coef: float = 0.2
-
-    lehv_interaction_strength_offset: float = 1.2
-    lehv_interaction_strength_coef: float = 0.3
-
-    hehv_attn_ent_coef: float = 0.2
-    hehv_attn_vent_offset: float = 2.0
-    hehv_attn_vent_coef: float = 0.5
-
-    # TODO not convinced this should
-    n_adaptive_samples: int = 5
-
-    # Adaptive sampling parameters
-    ada_temp_logits: float = 0.3
-    ada_temp_attn: float = 0.2
-    ada_temp_agree: float = 0.2
-    ada_top_p: float = 0.1
-    ada_top_k_int: float = 0.3
-    ada_top_k_agree: float = 0.2
-    ada_min_p: float = 0.5
-    ada_score_logits_ent: float = 0.1
-    ada_score_attn_ent: float = 0.2
-    ada_score_logits_vent: float = 0.3
-    ada_score_attn_vent: float = 0.4
-    ada_score_agree: float = 0.5
-    ada_score_int: float = 0.6
-
-
-def sample(gen_tokens: jax.Array, logits: jax.Array, attention_scores: jax.Array, cfg: SamplerConfig,
+    min_probability: float = 0.03  # Minimum probability threshold for token selection
+
+    # Logits Entropy Thresholds
+    low_logits_entropy_threshold: float = 0.01
+    medium_logits_entropy_threshold: float = 0.7
+    high_logits_entropy_threshold: float = 2.1
+
+    # Logits Varentropy Thresholds
+    low_logits_varentropy_threshold: float = 0.05
+    medium_logits_varentropy_threshold: float = 2.0
+    high_logits_varentropy_threshold: float = 5.8
+
+    # Attention Entropy Thresholds
+    low_attention_entropy_threshold: float = 11.915
+    medium_attention_entropy_threshold: float = 11.921
+    high_attention_entropy_threshold: float = 11.926
+
+    # Attention Varentropy Thresholds
+    low_attention_varentropy_threshold: float = 0.001
+    medium_attention_varentropy_threshold: float = 0.0045
+    high_attention_varentropy_threshold: float = 0.009
+
+    # Agreement Thresholds
+    low_agreement_threshold: float = 2e-06
+    medium_agreement_threshold: float = 4e-06
+    high_agreement_threshold: float = 5e-06
+
+    # Interaction Strength Thresholds
+    low_interaction_strength_threshold: float = 0.2
+    medium_interaction_strength_threshold: float = 0.247
+    high_interaction_strength_threshold: float = 0.264
+
+    # Offsets and Coefficients for Adjusting Sampling Parameters
+    high_entropy_attention_offset: float = 1.3
+    high_entropy_attention_coefficient: float = 0.2
+
+    low_entropy_interaction_strength_offset: float = 1.2
+    low_entropy_interaction_strength_coefficient: float = 0.3
+
+    high_entropy_varentropy_attention_offset: float = 2.0
+    high_entropy_varentropy_attention_coefficient: float = 0.5
+
+    # Adaptive Sampling Parameters
+    number_of_adaptive_samples: int = 5
+
+    adaptive_temperature_logits_coefficient: float = 0.3
+    adaptive_temperature_attention_coefficient: float = 0.2
+    adaptive_temperature_agreement_coefficient: float = 0.2
+    adaptive_top_p_coefficient: float = 0.1
+    adaptive_top_k_interaction_coefficient: float = 0.3
+    adaptive_top_k_agreement_coefficient: float = 0.2
+    adaptive_min_p_coefficient: float = 0.5
+    adaptive_score_logits_entropy_coefficient: float = 0.1
+    adaptive_score_attention_entropy_coefficient: float = 0.2
+    adaptive_score_logits_varentropy_coefficient: float = 0.3
+    adaptive_score_attention_varentropy_coefficient: float = 0.4
+    adaptive_score_agreement_coefficient: float = 0.5
+    adaptive_score_interaction_strength_coefficient: float = 0.6
+
+
+def sample(logits: jax.Array, attention_scores: jax.Array, cfg: SamplerConfig,
            clarifying_question_token: int = 2564, key=jax.random.PRNGKey(1337)) -> jax.Array:
 
     metrics = calculate_metrics(logits, attention_scores)
@@ -123,67 +138,136 @@ def sample(gen_tokens: jax.Array, logits: jax.Array, attention_scores: jax.Array
     attn_ent, attn_vent = metrics["attn_entropy"], metrics["attn_varentropy"]
     agreement = metrics["agreement"]
     interaction_strength = metrics["interaction_strength"]
+    #print(f'{metrics=}')
 
     # Low Entropy, Low Varentropy: "flowing with unspoken intent"
-    if ent < cfg.low_ent_thresh and vent < cfg.low_vent_thresh:
+    if (ent < cfg.low_logits_entropy_threshold and
+        vent < cfg.low_logits_varentropy_threshold and
+        attn_ent < cfg.low_attention_entropy_threshold and
+        attn_vent < cfg.low_attention_varentropy_threshold and
+        agreement < cfg.low_agreement_threshold and
+        interaction_strength < cfg.low_interaction_strength_threshold):
         return jnp.argmax(logits[:, -1], axis=-1, keepdims=True).astype(jnp.int32)
 
     # High Entropy, Low Varentropy: "treading carefully, asking clarifying questions"
-    elif ent > cfg.high_ent_thresh and vent < cfg.low_vent_thresh:
+    elif (ent > cfg.high_logits_entropy_threshold and
+          vent < cfg.low_logits_varentropy_threshold and
+          attn_ent < cfg.low_attention_entropy_threshold and
+          attn_vent < cfg.low_attention_varentropy_threshold and
+          agreement < cfg.low_agreement_threshold and
+          interaction_strength < cfg.low_interaction_strength_threshold):
         # Insert a clarifying question token if not already present
-        if not jnp.isin(gen_tokens[:,-1], clarifying_question_token).any():
-            return jnp.array([[clarifying_question_token]])
+        if not jnp.isin(gen_tokens[:, -1], clarifying_question_token).any():
+            return jnp.array([[clarifying_question_token]]), color
         else:
             # If we've just asked a question, sample with slightly higher temperature
-            temp_adj = cfg.helv_attn_ent_offset + cfg.helv_attn_ent_coef * attn_ent  # Increase temperature based on attention entropy
-            return _sample(logits, temperature=min(1.5, cfg.temp * temp_adj), top_p=cfg.top_p, top_k=cfg.top_k, min_p=cfg.min_p, key=key)
+            temp_adj = cfg.high_entropy_attention_offset + cfg.high_entropy_attention_coefficient * attn_ent  # Increase temperature based on attention entropy
+            return _sample(
+                logits,
+                temperature=min(1.5, cfg.temperature * temp_adj),
+                top_p=cfg.top_p,
+                top_k=cfg.top_k,
+                min_p=cfg.min_probability,
+                key=key
+            )
 
     # Low Entropy, High Varentropy: "exploring forks in the path"
-    elif ent < cfg.high_ent_thresh and vent > cfg.high_vent_thresh:
-        temp_adj = cfg.lehv_interaction_strength_offset + cfg.lehv_interaction_strength_coef * interaction_strength  # Increase temperature based on interaction strength
+    elif (ent < cfg.high_logits_entropy_threshold and
+          vent > cfg.high_logits_varentropy_threshold and
+          attn_ent < cfg.low_attention_entropy_threshold and
+          attn_vent > cfg.high_attention_varentropy_threshold and
+          agreement < cfg.low_agreement_threshold and
+          interaction_strength > cfg.low_interaction_strength_threshold):
+        temp_adj = cfg.low_entropy_interaction_strength_offset + cfg.low_entropy_interaction_strength_coefficient * interaction_strength  # Increase temperature based on interaction strength
         top_k_adj = max(5, int(cfg.top_k * (1 + 0.5 * (1 - agreement))))  # Increase top_k when agreement is low
-        return _sample(logits, temperature=min(1.5, cfg.temp * temp_adj), top_p=cfg.top_p, top_k=top_k_adj, min_p=cfg.min_p, key=key)
+        return _sample(
+            logits,
+            temperature=min(1.5, cfg.temperature * temp_adj),
+            top_p=cfg.top_p,
+            top_k=top_k_adj,
+            min_p=cfg.min_probability,
+            key=key
+        )
 
     # High Entropy, High Varentropy: "resampling in the mist"
-    elif ent > cfg.med_ent_thresh and vent > cfg.high_vent_thresh:
+    elif (ent > cfg.medium_logits_entropy_threshold and
+          vent > cfg.high_logits_varentropy_threshold and
+          attn_ent > cfg.high_attention_entropy_threshold and
+          attn_vent > cfg.high_attention_varentropy_threshold and
+          agreement > cfg.high_agreement_threshold and
+          interaction_strength > cfg.high_interaction_strength_threshold):
         # Use high temperature and adjusted top_p based on attention metrics
-        temp_adj = cfg.hehv_attn_vent_offset + cfg.hehv_attn_vent_coef * attn_vent  # Increase temperature based on attention varentropy
-        top_p_adj = max(0.5, cfg.top_p - cfg.hehv_attn_ent_coef * attn_ent)  # Decrease top_p when attention entropy is high
-        return _sample(logits, temperature=max(2.0, cfg.temp * temp_adj), top_p=top_p_adj, top_k=cfg.top_k, min_p=cfg.min_p, key=key)
+        temp_adj = cfg.high_entropy_varentropy_attention_offset + cfg.high_entropy_varentropy_attention_coefficient * attn_vent  # Increase temperature based on attention varentropy
+        top_p_adj = max(0.5, cfg.top_p - cfg.high_entropy_attention_coefficient * attn_ent)  # Decrease top_p when attention entropy is high
+        return _sample(
+            logits,
+            temperature=max(2.0, cfg.temperature * temp_adj),
+            top_p=top_p_adj,
+            top_k=cfg.top_k,
+            min_p=cfg.min_probability,
+            key=key
+        )
 
     # Middle ground: use adaptive sampling
     else:
         logits_uncertainty = metrics["logits_entropy"] + metrics["logits_varentropy"]
         attn_uncertainty = metrics["attn_entropy"] + metrics["attn_varentropy"]
 
-        temperature = cfg.temp * (1 + cfg.ada_temp_logits * logits_uncertainty + cfg.ada_temp_attn * attn_uncertainty - cfg.ada_temp_agree * metrics["agreement"])
-        top_p = jnp.clip(cfg.top_p * (1 + cfg.ada_top_p * metrics["attn_varentropy"]), 0.1, 1.0)
+        temperature = cfg.temperature * (
+            1 +
+            cfg.adaptive_temperature_logits_coefficient * ent +
+            cfg.adaptive_temperature_attention_coefficient * attn_ent -
+            cfg.adaptive_temperature_agreement_coefficient * agreement
+        )
+        top_p = jnp.clip(
+            cfg.top_p * (1 + cfg.adaptive_top_p_coefficient * attn_vent),
+            0.1,
+            1.0
+        )
         top_k = int(jnp.clip(
-            jnp.round(cfg.top_k * (1 + cfg.ada_top_k_int * metrics["interaction_strength"].item() - cfg.ada_top_k_agree * metrics["agreement"].item())),
+            jnp.round(cfg.top_k * (
+                1 +
+                cfg.adaptive_top_k_interaction_coefficient * interaction_strength.item() -
+                cfg.adaptive_top_k_agreement_coefficient * agreement.item()
+            )),
             a_min=1,
             a_max=100
         ))
-        min_p = jnp.clip(cfg.min_p * (1 - cfg.ada_min_p * logits_uncertainty), 0.01, 0.5)
+        min_p = jnp.clip(
+            cfg.min_probability * (1 - cfg.adaptive_min_p_coefficient * vent),
+            0.01,
+            0.5
+        )
 
-        keys = jax.random.split(key, cfg.n_adaptive_samples)
+        keys = jax.random.split(key, cfg.number_of_adaptive_samples)
 
         samples = []
         for sample_key in keys:
-            sample = _sample(logits, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, key=sample_key)
+            sample = _sample(
+                logits,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                min_p=min_p,
+                key=sample_key
+            )
             samples.append(sample)
 
         def score_sample(sample):
-            log_prob = jnp.sum(jax.nn.log_softmax(logits) * jax.nn.one_hot(sample, logits.shape[-1]))
+            log_prob = jnp.sum(
+                jax.nn.log_softmax(logits) * jax.nn.one_hot(sample, logits.shape[-1]),
+                axis=-1
+            )
             confidence_score = (
-                (1 - metrics["logits_entropy"]) * cfg.ada_score_logits_ent +
-                (1 - metrics["attn_entropy"]) * cfg.ada_score_attn_ent +
-                (1 - metrics["logits_varentropy"]) * cfg.ada_score_logits_vent +
-                (1 - metrics["attn_varentropy"]) * cfg.ada_score_attn_vent +
-                metrics["agreement"] * cfg.ada_score_agree +
-                metrics["interaction_strength"] * cfg.ada_score_int
+                (1 - ent / cfg.high_logits_entropy_threshold) * cfg.adaptive_score_logits_entropy_coefficient +
+                (1 - attn_ent / cfg.high_attention_entropy_threshold) * cfg.adaptive_score_attention_entropy_coefficient +
+                (1 - vent / cfg.high_logits_varentropy_threshold) * cfg.adaptive_score_logits_varentropy_coefficient +
+                (1 - attn_vent / cfg.high_attention_varentropy_threshold) * cfg.adaptive_score_attention_varentropy_coefficient +
+                (agreement / cfg.high_agreement_threshold) * cfg.adaptive_score_agreement_coefficient +
+                (interaction_strength / cfg.high_interaction_strength_threshold) * cfg.adaptive_score_interaction_strength_coefficient
             )
             return log_prob + confidence_score
 
-        sample_scores = [score_sample(sample) for sample in samples]
-        best_sample_idx = jnp.argmax(jnp.array(sample_scores))
+        sample_scores = jnp.array([score_sample(sample) for sample in samples])
+        best_sample_idx = jnp.argmax(sample_scores)
         return samples[best_sample_idx]
diff --git a/entropix/mcts.py b/entropix/torch_mcts.py
similarity index 100%
rename from entropix/mcts.py
rename to entropix/torch_mcts.py
diff --git a/entropix/weights.py b/entropix/weights.py
index a8b2617..018fea3 100644
--- a/entropix/weights.py
+++ b/entropix/weights.py
@@ -3,6 +3,11 @@
 import jax
 import jax.numpy as jnp
 
+from jax.sharding import Mesh
+from jax.sharding import NamedSharding
+from jax.sharding import PartitionSpec as PS
+from jax.experimental import mesh_utils
+
 from pathlib import Path
 
 
@@ -25,36 +30,58 @@ class XfmrWeights(NamedTuple):
   layer_weights: List[LayerWeights]
 
 
-def load_weights(ckpt_dir: Path, n_layers: int = 16):
-  w = {}
-  layer_weights = []
-  try:
-    device = jax.devices("gpu")[0]
-  except RuntimeError:
-    print("GPU not found. Using CPU instead.")
-    device = jax.devices("cpu")[0]
-  for file in ckpt_dir.glob("*.npy"):
-    name = '.'.join(str(file).split('/')[-1].split('.')[:-1])
-    weight = jnp.load(file=file, mmap_mode='r', allow_pickle=True)
-    w[name] = jax.device_put(weight, device)
-  for i in range(n_layers):
-    layer_weights.append(LayerWeights(
-      wq=w[f'layers.{i}.attention.wq.weight'],
-      wk=w[f'layers.{i}.attention.wk.weight'],
-      wv=w[f'layers.{i}.attention.wv.weight'],
-      wo=w[f'layers.{i}.attention.wo.weight'],
-      w1=w[f'layers.{i}.feed_forward.w1.weight'],
-      w2=w[f'layers.{i}.feed_forward.w2.weight'],
-      w3=w[f'layers.{i}.feed_forward.w3.weight'],
-      ffn_norm=w[f'layers.{i}.ffn_norm.weight'],
-      attention_norm=w[f'layers.{i}.attention_norm.weight'],
-    ))
-
-  xfmr_weights = XfmrWeights(
-    tok_embeddings=w['tok_embeddings.weight'],
-    norm=w['norm.weight'],
-    output=w['output.weight'],
-    layer_weights=layer_weights
-  )
-
-  return xfmr_weights
+def create_partition_spec(key):
+    dp = 'dp'
+    mp = 'mp'
+    fsdp = 'fsdp'
+    if 'norm' in key:
+        return PS()
+    if 'rope.freqs' in key:
+        return PS()
+    elif 'tok_embeddings' in key:
+        return PS(fsdp, mp)
+    elif 'output' in key:
+        return PS(fsdp, mp)
+    elif 'w2' in key:
+        return PS(fsdp, mp)
+    else:
+        return PS(mp, fsdp)
+
+
+# def load_weights(ckpt_dir: Path, n_layers: int = 80):
+# def load_weights(ckpt_dir: Path, n_layers: int = 16):
+def load_weights(ckpt_dir: Path, n_layers: int = 28):
+    w = {}
+    layer_weights = []
+    # mesh = jax.sharding.Mesh(mesh_utils.create_device_mesh((1, 8)), ('mp', 'fsdp'))
+    # mesh = jax.sharding.Mesh(mesh_utils.create_device_mesh((1, 2)), ('mp', 'fsdp'))
+    mesh = jax.sharding.Mesh(mesh_utils.create_device_mesh((1, 1)), ('mp', 'fsdp'))
+    
+    for file in ckpt_dir.glob("*.npy"):
+        name = '.'.join(str(file).split('/')[-1].split('.')[:-1])
+        weight = jnp.load(file=file, mmap_mode='r', allow_pickle=True)
+        partition_spec = create_partition_spec(name)
+        sharding = NamedSharding(mesh, partition_spec)
+        w[name] = jax.device_put(weight, sharding)
+    
+    for i in range(n_layers):
+        layer_weights.append(LayerWeights(
+            wq=w[f'layers.{i}.attention.wq.weight'],
+            wk=w[f'layers.{i}.attention.wk.weight'],
+            wv=w[f'layers.{i}.attention.wv.weight'],
+            wo=w[f'layers.{i}.attention.wo.weight'],
+            w1=w[f'layers.{i}.feed_forward.w1.weight'],
+            w2=w[f'layers.{i}.feed_forward.w2.weight'],
+            w3=w[f'layers.{i}.feed_forward.w3.weight'],
+            ffn_norm=w[f'layers.{i}.ffn_norm.weight'],
+            attention_norm=w[f'layers.{i}.attention_norm.weight'],
+        ))
+    
+    xfmr_weights = XfmrWeights(
+        tok_embeddings=w['tok_embeddings.weight'],
+        norm=w['norm.weight'],
+        output=w['output.weight'],
+        layer_weights=layer_weights
+    )
+
+    return xfmr_weights
diff --git a/poetry.lock b/poetry.lock
index 4ff4444..5d91fed 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -11,6 +11,173 @@ files = [
     {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.0.1"
+description = "Accelerate"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "accelerate-1.0.1-py3-none-any.whl", hash = "sha256:c6aa0c7b8a797cb150471e90e3ca36ac41f5d4b40512cdd6f058b8bf25589467"},
+    {file = "accelerate-1.0.1.tar.gz", hash = "sha256:e8f95fc2db14915dc0a9182edfcf3068e5ddb2fa310b583717ad44e5c442399c"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.21.0"
+numpy = ">=1.17,<3.0.0"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = ">=0.4.3"
+torch = ">=1.10.0"
+
+[package.extras]
+deepspeed = ["deepspeed"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.6.4,<0.7.0)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.10.10"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
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 [[package]]
@@ -169,6 +460,20 @@ setuptools = {version = "*", markers = "python_version >= \"3.12\""}
 toolz = ">=0.9.0"
 typing-extensions = ">=4.2.0"
 
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -180,6 +485,82 @@ files = [
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 ]
 
+[[package]]
+name = "dataproperty"
+version = "1.0.1"
+description = "Python library for extract property from data."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+mbstrdecoder = ">=1.0.0,<2"
+typepy = {version = ">=1.2.0,<2", extras = ["datetime"]}
+
+[package.extras]
+logging = ["loguru (>=0.4.1,<1)"]
+test = ["pytest (>=6.0.1)", "pytest-md-report (>=0.3)", "tcolorpy (>=0.1.2)"]
+
+[[package]]
+name = "datasets"
+version = "3.0.1"
+description = "HuggingFace community-driven open-source library of datasets"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+]
+
+[package.dependencies]
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+filelock = "*"
+fsspec = {version = ">=2023.1.0,<=2024.6.1", extras = ["http"]}
+huggingface-hub = ">=0.22.0"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=15.0.0"
+pyyaml = ">=5.1"
+requests = ">=2.32.2"
+tqdm = ">=4.66.3"
+xxhash = "*"
+
+[package.extras]
+audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0)"]
+benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
+dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "torchdata", "transformers", "transformers (>=4.42.0)", "zstandard"]
+docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
+jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
+quality = ["ruff (>=0.3.0)"]
+s3 = ["s3fs"]
+tensorflow = ["tensorflow (>=2.6.0)"]
+tensorflow-gpu = ["tensorflow (>=2.6.0)"]
+tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "torchdata", "transformers (>=4.42.0)", "zstandard"]
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+torch = ["torch"]
+vision = ["Pillow (>=9.4.0)"]
+
+[[package]]
+name = "dill"
+version = "0.3.8"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
+    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
+]
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
+
 [[package]]
 name = "docstring-parser"
 version = "0.16"
@@ -228,6 +609,41 @@ etree-jax = ["etils[etree]", "jax[cpu]"]
 etree-tf = ["etils[etree]", "tensorflow"]
 lazy-imports = ["etils[ecolab]"]
 
+[[package]]
+name = "evaluate"
+version = "0.4.3"
+description = "HuggingFace community-driven open-source library of evaluation"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+    {file = "evaluate-0.4.3.tar.gz", hash = "sha256:3a5700cf83aabee9549264e1e5666f116367c61dbd4d38352015e859a5e2098d"},
+]
+
+[package.dependencies]
+datasets = ">=2.0.0"
+dill = "*"
+fsspec = {version = ">=2021.05.0", extras = ["http"]}
+huggingface-hub = ">=0.7.0"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+requests = ">=2.19.0"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+dev = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "jiwer", "mauve-text", "nltk (<3.9)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy (>=1.10.0)", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+docs = ["s3fs"]
+evaluator = ["scipy (>=1.7.1)", "transformers"]
+quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
+template = ["cookiecutter", "gradio (>=3.0.0)"]
+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "jiwer", "mauve-text", "nltk (<3.9)", "pytest", "pytest-datadir", "pytest-xdist", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy (>=1.10.0)", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+torch = ["torch"]
+
 [[package]]
 name = "fairscale"
 version = "0.4.13"
@@ -289,17 +705,106 @@ dev = ["pre-commit (>=3.8.0)"]
 docs = ["Pygments (>=2.6.1)", "dm-haiku", "docutils (==0.16)", "einops", "ipykernel", "ipython-genutils", "jupytext (==1.13.8)", "matplotlib", "ml-collections", "myst-nb", "nbstripout", "recommonmark", "scikit-learn", "sphinx (>=3.3.1)", "sphinx-book-theme", "sphinx-design"]
 testing = ["clu", "clu (<=0.0.9)", "einops", "gymnasium[accept-rom-license,atari]", "jaxlib", "jaxtyping", "jraph (>=0.0.6dev0)", "ml-collections", "mypy", "opencv-python", "pytest", "pytest-cov", "pytest-custom-exit-code", "pytest-xdist", "pytype", "sentencepiece", "tensorflow (>=2.12.0)", "tensorflow-datasets", "tensorflow-text (>=2.11.0)", "torch", "treescope (>=0.1.1)"]
 
+[[package]]
+name = "frozenlist"
+version = "1.4.1"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
 [[package]]
 name = "fsspec"
-version = "2024.9.0"
+version = "2024.6.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -330,13 +835,13 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.25.1"
+version = "0.25.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -391,6 +896,17 @@ files = [
 [package.extras]
 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
+[[package]]
+name = "immutabledict"
+version = "4.2.0"
+description = "Immutable wrapper around dictionaries (a fork of frozendict)"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
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+]
+
 [[package]]
 name = "importlib-resources"
 version = "6.4.5"
@@ -572,6 +1088,98 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[[package]]
+name = "jsonlines"
+version = "4.0.0"
+description = "Library with helpers for the jsonlines file format"
+optional = false
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+[package.dependencies]
+chardet = ">=3.0.4,<6"
+
+[package.extras]
+test = ["Faker (>=1.0.2)", "pytest (>=6.0.1)", "pytest-md-report (>=0.1)"]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -870,6 +1496,17 @@ numpy = [
 [package.extras]
 dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
 
+[[package]]
+name = "more-itertools"
+version = "10.5.0"
+description = "More routines for operating on iterables, beyond itertools"
+optional = false
+python-versions = ">=3.8"
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+]
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
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+]
+
+[package.dependencies]
+numpy = ">=1.23.0"
+
 [[package]]
 name = "numpy"
 version = "2.1.2"
@@ -1053,24 +1882,26 @@ files = [
 
 [[package]]
 name = "nvidia-cublas-cu12"
-version = "12.1.3.1"
+version = "12.4.5.8"
 description = "CUBLAS native runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
 name = "nvidia-cuda-cupti-cu12"
-version = "12.1.105"
+version = "12.4.127"
 description = "CUDA profiling tools runtime libs."
 optional = false
 python-versions = ">=3"
 files = [
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+    {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:79279b35cf6f91da114182a5ce1864997fd52294a87a16179ce275773799458a"},
+    {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:9dec60f5ac126f7bb551c055072b69d85392b13311fcc1bcda2202d172df30fb"},
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 ]
 
 [[package]]
@@ -1087,24 +1918,26 @@ files = [
 
 [[package]]
 name = "nvidia-cuda-nvrtc-cu12"
-version = "12.1.105"
+version = "12.4.127"
 description = "NVRTC native runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
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+    {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a178759ebb095827bd30ef56598ec182b85547f1508941a3d560eb7ea1fbf338"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:a961b2f1d5f17b14867c619ceb99ef6fcec12e46612711bcec78eb05068a60ec"},
 ]
 
 [[package]]
 name = "nvidia-cuda-runtime-cu12"
-version = "12.1.105"
+version = "12.4.127"
 description = "CUDA Runtime native Libraries"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
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+    {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3"},
+    {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64403288fa2136ee8e467cdc9c9427e0434110899d07c779f25b5c068934faa5"},
+    {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:09c2e35f48359752dfa822c09918211844a3d93c100a715d79b59591130c5e1e"},
 ]
 
 [[package]]
@@ -1123,35 +1956,41 @@ nvidia-cublas-cu12 = "*"
 
 [[package]]
 name = "nvidia-cufft-cu12"
-version = "11.0.2.54"
+version = "11.2.1.3"
 description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
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+    {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399"},
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+    {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-win_amd64.whl", hash = "sha256:d802f4954291101186078ccbe22fc285a902136f974d369540fd4a5333d1440b"},
 ]
 
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
 [[package]]
 name = "nvidia-curand-cu12"
-version = "10.3.2.106"
+version = "10.3.5.147"
 description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
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+    {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a88f583d4e0bb643c49743469964103aa59f7f708d862c3ddb0fc07f851e3b8b"},
+    {file = "nvidia_curand_cu12-10.3.5.147-py3-none-win_amd64.whl", hash = "sha256:f307cc191f96efe9e8f05a87096abc20d08845a841889ef78cb06924437f6771"},
 ]
 
 [[package]]
 name = "nvidia-cusolver-cu12"
-version = "11.4.5.107"
+version = "11.6.1.9"
 description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
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+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d338f155f174f90724bbde3758b7ac375a70ce8e706d70b018dd3375545fc84e"},
+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260"},
+    {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-win_amd64.whl", hash = "sha256:e77314c9d7b694fcebc84f58989f3aa4fb4cb442f12ca1a9bde50f5e8f6d1b9c"},
 ]
 
 [package.dependencies]
@@ -1161,13 +2000,14 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-cusparse-cu12"
-version = "12.1.0.106"
+version = "12.3.1.170"
 description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+    {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_aarch64.whl", hash = "sha256:9d32f62896231ebe0480efd8a7f702e143c98cfaa0e8a76df3386c1ba2b54df3"},
+    {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1"},
+    {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-win_amd64.whl", hash = "sha256:9bc90fb087bc7b4c15641521f31c0371e9a612fc2ba12c338d3ae032e6b6797f"},
 ]
 
 [package.dependencies]
@@ -1175,36 +2015,35 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.20.5"
+version = "2.21.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
-    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
+    {file = "nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:8579076d30a8c24988834445f8d633c697d42397e92ffc3f63fa26766d25e0a0"},
 ]
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.6.77"
+version = "12.4.127"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:3bf10d85bb1801e9c894c6e197e44dd137d2a0a9e43f8450e9ad13f2df0dd52d"},
-    {file = "nvidia_nvjitlink_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:9ae346d16203ae4ea513be416495167a0101d33d2d14935aa9c1829a3fb45142"},
-    {file = "nvidia_nvjitlink_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:410718cd44962bed862a31dd0318620f6f9a8b28a6291967bcfcb446a6516771"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:fd9020c501d27d135f983c6d3e244b197a7ccad769e34df53a42e276b0e25fa1"},
 ]
 
 [[package]]
 name = "nvidia-nvtx-cu12"
-version = "12.1.105"
+version = "12.4.127"
 description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+    {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7959ad635db13edf4fc65c06a6e9f9e55fc2f92596db928d169c0bb031e88ef3"},
+    {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:781e950d9b9f60d8241ccea575b32f5105a5baf4c2351cab5256a24869f12a1a"},
+    {file = "nvidia_nvtx_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:641dccaaa1139f3ffb0d3164b4b84f9d253397e38246a4f2f36728b48566d485"},
 ]
 
 [[package]]
@@ -1245,21 +2084,20 @@ test = ["dm-tree (>=0.1.7)", "flax (>=0.5.3)", "scikit-learn", "scipy (>=1.7.1)"
 
 [[package]]
 name = "orbax-checkpoint"
-version = "0.6.4"
+version = "0.7.0"
 description = "Orbax Checkpoint"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "orbax_checkpoint-0.6.4-py3-none-any.whl", hash = "sha256:b4f2608ee4d1da67f7619fc35ff9c928ecdf4ccf7546eeb43ecf38c2608b6dea"},
-    {file = "orbax_checkpoint-0.6.4.tar.gz", hash = "sha256:366b4d528a7322e1b3d9ddcaed45c8515add0d2fc69c8975c30d98638543240f"},
+    {file = "orbax_checkpoint-0.7.0-py3-none-any.whl", hash = "sha256:0469030dd70729f7416981712a9ea8a82bd02c65ca82c933675c9e3ed4763f9b"},
+    {file = "orbax_checkpoint-0.7.0.tar.gz", hash = "sha256:f5a59babbf86fdafacddcfd2fb1c6d45b4fa0685b38a87a4598a5702bb70a657"},
 ]
 
 [package.dependencies]
 absl-py = "*"
 etils = {version = "*", extras = ["epath", "epy"]}
 humanize = "*"
-jax = ">=0.4.26"
-jaxlib = "*"
+jax = ">=0.4.34"
 msgpack = "*"
 nest_asyncio = "*"
 numpy = "*"
@@ -1282,6 +2120,136 @@ files = [
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+
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+description = "Parameter-Efficient Fine-Tuning (PEFT)"
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+
 [[package]]
 name = "pfzy"
 version = "0.3.4"
@@ -1311,6 +2279,25 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
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+
 [[package]]
 name = "prompt-toolkit"
 version = "3.0.48"
@@ -1325,6 +2312,113 @@ files = [
 [package.dependencies]
 wcwidth = "*"
 
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+
 [[package]]
 name = "ruff"
 version = "0.6.9"
@@ -1775,6 +3078,30 @@ files = [
     {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
 ]
 
+[[package]]
+name = "sacrebleu"
+version = "2.4.3"
+description = "Hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "sacrebleu-2.4.3-py3-none-any.whl", hash = "sha256:a976fd6998d8ced267a722120ec7fc47083c8e9745d8808ccee6424464a0aa31"},
+    {file = "sacrebleu-2.4.3.tar.gz", hash = "sha256:e734b1e0baeaea6ade0fefc9d23bac3df50bf15775d8b78edc108db63654192a"},
+]
+
+[package.dependencies]
+colorama = "*"
+lxml = "*"
+numpy = ">=1.17"
+portalocker = "*"
+regex = "*"
+tabulate = ">=0.8.9"
+
+[package.extras]
+dev = ["lxml-stubs", "mypy", "pytest", "setuptools", "types-tabulate", "wheel"]
+ja = ["ipadic (>=1.0,<2.0)", "mecab-python3 (>=1.0.9,<2.0.0)"]
+ko = ["mecab-ko (>=1.0.0,<=1.0.1)", "mecab-ko-dic (>=1.0,<2.0)"]
+
 [[package]]
 name = "safetensors"
 version = "0.4.5"
@@ -1907,6 +3234,56 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
+[[package]]
+name = "scikit-learn"
+version = "1.5.2"
+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.9"
+files = [
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+[package.dependencies]
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+numpy = ">=1.19.5"
+scipy = ">=1.6.0"
+threadpoolctl = ">=3.1.0"
+
+[package.extras]
+benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-design (>=0.6.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
+examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
+install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
+maintenance = ["conda-lock (==2.5.6)"]
+tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
+
 [[package]]
 name = "scipy"
 version = "1.14.1"
@@ -1959,13 +3336,13 @@ test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.3
 
 [[package]]
 name = "setuptools"
-version = "75.1.0"
+version = "75.2.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "setuptools-75.2.0-py3-none-any.whl", hash = "sha256:a7fcb66f68b4d9e8e66b42f9876150a3371558f98fa32222ffaa5bced76406f8"},
+    {file = "setuptools-75.2.0.tar.gz", hash = "sha256:753bb6ebf1f465a1912e19ed1d41f403a79173a9acf66a42e7e6aec45c3c16ec"},
 ]
 
 [package.extras]
@@ -1991,15 +3368,36 @@ files = [
 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
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+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "sqlitedict"
+version = "2.1.0"
+description = "Persistent dict in Python, backed up by sqlite3 and pickle, multithread-safe."
+optional = false
+python-versions = "*"
+files = [
+    {file = "sqlitedict-2.1.0.tar.gz", hash = "sha256:03d9cfb96d602996f1d4c2db2856f1224b96a9c431bdd16e78032a72940f9e8c"},
+]
+
 [[package]]
 name = "sympy"
-version = "1.13.3"
+version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.13.3-py3-none-any.whl", hash = "sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73"},
-    {file = "sympy-1.13.3.tar.gz", hash = "sha256:b27fd2c6530e0ab39e275fc9b683895367e51d5da91baa8d3d64db2565fec4d9"},
+    {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
+    {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
 ]
 
 [package.dependencies]
@@ -2008,6 +3406,53 @@ mpmath = ">=1.1.0,<1.4"
 [package.extras]
 dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
+[[package]]
+name = "tabledata"
+version = "1.3.3"
+description = "tabledata is a Python library to represent tabular data. Used for pytablewriter/pytablereader/SimpleSQLite/etc."
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "tabledata-1.3.3.tar.gz", hash = "sha256:c90daaba9a408e4397934b3ff2f6c06797d5289676420bf520c741ad43e6ff91"},
+]
+
+[package.dependencies]
+DataProperty = ">=1.0.1,<2"
+typepy = ">=1.2.0,<2"
+
+[package.extras]
+logging = ["loguru (>=0.4.1,<1)"]
+test = ["pytablewriter (>=0.46)", "pytest"]
+
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+description = "Pretty-print tabular data"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"},
+]
+
+[package.extras]
+widechars = ["wcwidth"]
+
+[[package]]
+name = "tcolorpy"
+version = "0.1.6"
+description = "tcolopy is a Python library to apply true color for terminal text."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+test = ["pytest (>=6.0.1)", "pytest-md-report (>=0.5)"]
+
 [[package]]
 name = "tensorstore"
 version = "0.1.66"
@@ -2042,6 +3487,17 @@ files = [
 ml-dtypes = ">=0.3.1"
 numpy = ">=1.22.0"
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "tiktoken"
 version = "0.4.0"
@@ -2089,111 +3545,111 @@ blobfile = ["blobfile (>=2)"]
 
 [[package]]
 name = "tokenizers"
-version = "0.20.0"
+version = "0.20.1"
 description = ""
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -2217,31 +3673,20 @@ files = [
 
 [[package]]
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 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
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 ]
 
 [package.dependencies]
@@ -2249,25 +3694,31 @@ filelock = "*"
 fsspec = "*"
 jinja2 = "*"
 networkx = "*"
-nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cublas-cu12 = {version = "12.4.5.8", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cudnn-cu12 = {version = "9.1.0.70", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-setuptools = "*"
-sympy = "*"
-triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
+nvidia-cufft-cu12 = {version = "11.2.1.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.5.147", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.6.1.9", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.3.1.170", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.21.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvjitlink-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sympy = {version = "1.13.1", markers = "python_version >= \"3.9\""}
+triton = {version = "3.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.11.0)"]
+optree = ["optree (>=0.12.0)"]
+
+[package.source]
+type = "legacy"
+url = "https://download.pytorch.org/whl/cu124"
+reference = "pytorch"
 
 [[package]]
 name = "tqdm"
@@ -2289,15 +3740,33 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "tqdm-multiprocess"
+version = "0.0.11"
+description = "Easy multiprocessing with tqdm and logging redirected to main process."
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
+colorama = "*"
+tqdm = "*"
+
+[package.extras]
+dev = ["twine"]
+
 [[package]]
 name = "transformers"
-version = "4.45.1"
+version = "4.45.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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+    {file = "transformers-4.45.2-py3-none-any.whl", hash = "sha256:c551b33660cfc815bae1f9f097ecfd1e65be623f13c6ee0dda372bd881460210"},
+    {file = "transformers-4.45.2.tar.gz", hash = "sha256:72bc390f6b203892561f05f86bbfaa0e234aab8e927a83e62b9d92ea7e3ae101"},
 ]
 
 [package.dependencies]
@@ -2360,16 +3829,16 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "3.0.0"
+version = "3.1.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
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+    {file = "triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc"},
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+    {file = "triton-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aafa9a20cd0d9fee523cd4504aa7131807a864cd77dcf6efe7e981f18b8c6c11"},
 ]
 
 [package.dependencies]
@@ -2380,6 +3849,27 @@ build = ["cmake (>=3.20)", "lit"]
 tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
+[[package]]
+name = "typepy"
+version = "1.3.2"
+description = "typepy is a Python library for variable type checker/validator/converter at a run time."
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "typepy-1.3.2.tar.gz", hash = "sha256:b69fd48b9f50cdb3809906eef36b855b3134ff66c8893a4f8580abddb0b39517"},
+]
+
+[package.dependencies]
+mbstrdecoder = ">=1.0.0,<2"
+packaging = {version = "*", optional = true, markers = "extra == \"datetime\""}
+python-dateutil = {version = ">=2.8.0,<3.0.0", optional = true, markers = "extra == \"datetime\""}
+pytz = {version = ">=2018.9", optional = true, markers = "extra == \"datetime\""}
+
+[package.extras]
+datetime = ["packaging", "python-dateutil (>=2.8.0,<3.0.0)", "pytz (>=2018.9)"]
+test = ["packaging", "pytest (>=6.0.1)", "python-dateutil (>=2.8.0,<3.0.0)", "pytz (>=2018.9)", "tcolorpy"]
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -2393,13 +3883,13 @@ files = [
 
 [[package]]
 name = "tyro"
-version = "0.8.11"
+version = "0.8.12"
 description = "Strongly typed, zero-effort CLI interfaces"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "tyro-0.8.11.tar.gz", hash = "sha256:486e6ed6672df2222303877a39a498e8f0fc762cdb9deacac67320c44a8eba45"},
+    {file = "tyro-0.8.12-py3-none-any.whl", hash = "sha256:ed98bf9f523bd48ca8618f9c823952532d713ba16c01b2a1662e4fa219c75f57"},
+    {file = "tyro-0.8.12.tar.gz", hash = "sha256:7475b196f1e578a297382f59d5a576cab897890b854801e111b7fb22be6584e1"},
 ]
 
 [package.dependencies]
@@ -2412,6 +3902,17 @@ typing-extensions = ">=4.7.0"
 [package.extras]
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+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
+]
+
 [[package]]
 name = "urllib3"
 version = "2.2.3"
@@ -2440,6 +3941,244 @@ files = [
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 ]
 
+[[package]]
+name = "word2number"
+version = "1.1"
+description = "Convert number words eg. three hundred and forty two to numbers (342)."
+optional = false
+python-versions = "*"
+files = [
+    {file = "word2number-1.1.zip", hash = "sha256:70e27a5d387f67b04c71fbb7621c05930b19bfd26efd6851e6e0f9969dcde7d0"},
+]
+
+[[package]]
+name = "xxhash"
+version = "3.5.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "zstandard-0.23.0.tar.gz", hash = "sha256:b2d8c62d08e7255f68f7a740bae85b3c9b8e5466baa9cbf7f57f1cde0ac6bc09"},
+]
+
+[package.dependencies]
+cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
+
+[package.extras]
+cffi = ["cffi (>=1.11)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "928daf7731d8a9fcbc0148f54e346ca450382fb1c7f4c359007d8d2dc90ea2f1"
+content-hash = "3004e91bfff9658d16beae3e88c37f2cebdd2d6d58798357ac71ca6083927595"
diff --git a/pyproject.toml b/pyproject.toml
index 264188d..922c4f2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,12 @@ torch = "^2.4.1"
 fairscale = "^0.4.13"
 
 
+
+[tool.poetry.group.eval.dependencies]
+lm-eval = "^0.4.5"
+langdetect = "^1.0.9"
+immutabledict = "^4.2.0"
+
 [[tool.poetry.source]]
 name = "pytorch"
 url = "https://download.pytorch.org/whl/cu124"
diff --git a/ui/.gitignore b/ui/.gitignore
index fd3dbb5..136b299 100644
--- a/ui/.gitignore
+++ b/ui/.gitignore
@@ -34,3 +34,5 @@ yarn-error.log*
 # typescript
 *.tsbuildinfo
 next-env.d.ts
+
+entropix/data/*.jsonl
\ No newline at end of file