diff --git a/tools/checkpoint/rwkv/run_lm_eval.py b/tools/checkpoint/rwkv/run_lm_eval.py
new file mode 100644
index 000000000..dc9b39da6
--- /dev/null
+++ b/tools/checkpoint/rwkv/run_lm_eval.py
@@ -0,0 +1,184 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+# pip install rwkv lm_eval --upgrade
+
+import os, sys, types, json, math, time
+from tqdm import tqdm
+import numpy as np
+np.set_printoptions(precision=4, suppress=True, linewidth=200)
+
+import torch
+from torch.nn import functional as F
+
+os.environ["RWKV_V7_ON"] = '1'
+os.environ["RWKV_JIT_ON"] = "1"
+os.environ["RWKV_CUDA_ON"] = "1"
+from rwkv.model import RWKV
+from rwkv.utils import PIPELINE
+
+from lm_eval.api.model import LM
+from lm_eval.api.instance import Instance
+from lm_eval.tasks import get_task_dict
+from lm_eval.evaluator import simple_evaluate
+
+########################################################################################################
+
+if len(sys.argv) < 2:
+    print("Usage: python your_script_name.py /path/to/your/model.pth")
+    sys.exit(1)
+
+MODEL_NAME = sys.argv[1]
+
+print(f'Loading model - {MODEL_NAME}')
+model = RWKV(model=MODEL_NAME, strategy='cuda fp16')
+pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
+
+eval_tasks = [
+    'lambada_openai', 'piqa', 'storycloze_2016', 'hellaswag', 'winogrande',
+    'arc_challenge', 'arc_easy', 'headqa_en', 'openbookqa', 'sciq',
+    'mmlu','glue']
+num_fewshot = 0
+
+RWKV_PAD = pipeline.tokenizer.encode('\n')
+STOP_TOKEN = pipeline.tokenizer.encode('\n\n')
+print('RWKV_PAD', RWKV_PAD)
+print('STOP_TOKEN', STOP_TOKEN)
+
+########################################################################################################
+
+class EvalHarnessAdapter(LM):
+    def __init__(self):
+        super().__init__()
+        self.tokenizer = pipeline.tokenizer
+        self.model = model
+
+    @property
+    def eot_token_id(self):
+        # End of text token
+        return self.tokenizer.encode('\n\n')[0]
+
+    @property
+    def max_length(self):
+        return 4096
+
+    @property
+    def max_gen_toks(self):
+        return 256
+
+    @property
+    def batch_size(self):
+        return 1
+
+    @property
+    def device(self):
+        return "cuda"
+
+    def tok_encode(self, string: str):
+        return self.tokenizer.encode(string)
+
+    def tok_decode(self, tokens):
+        return self.tokenizer.decode(tokens)
+
+    @torch.no_grad()
+    def loglikelihood(self, requests):
+        res = []
+        for request in tqdm(requests, "Running loglikelihood requests"):
+            context, continuation = request.args
+            context_enc = self.tok_encode(context)
+            continuation_enc = self.tok_encode(continuation)
+
+            full_enc = context_enc + continuation_enc
+            outputs, _ = self.model.forward(full_enc, None, full_output=True)
+            log_probs = F.log_softmax(outputs, dim=-1).cpu()
+
+            continuation_log_likelihood = 0.0
+            for i in range(len(continuation_enc)):
+                token_id = continuation_enc[i]
+                token_log_prob = log_probs[len(context_enc) - 1 + i, token_id]
+                continuation_log_likelihood += token_log_prob
+
+            last_token_logits = outputs[len(full_enc) - 2].float()
+            pred_token = torch.argmax(last_token_logits).item()
+            is_greedy = (pred_token == continuation_enc[-1])
+            
+            res.append((continuation_log_likelihood.item(), is_greedy))
+        return res
+
+    @torch.no_grad()
+    def loglikelihood_rolling(self, requests):
+        loglikelihoods = []
+        for request in tqdm(requests, "Running loglikelihood_rolling requests"):
+            string, = request.args
+            tokens = self.tok_encode(string)
+            
+            if not tokens:
+                loglikelihoods.append(0.0)
+                continue
+
+            outputs, _ = self.model.forward(tokens, None, full_output=True)
+            log_probs = F.log_softmax(outputs, dim=-1).cpu()
+
+            total_log_likelihood = 0.0
+            for i in range(1, len(tokens)):
+                token_id = tokens[i]
+                total_log_likelihood += log_probs[i - 1, token_id]
+            
+            loglikelihoods.append(total_log_likelihood.item())
+        
+        return loglikelihoods
+
+    @torch.no_grad()
+    def generate_until(self, requests):
+        res = []
+        for request in tqdm(requests, "Running generation requests"):
+            context = request.args[0]
+            gen_kwargs = request.args[1]
+            until = gen_kwargs.get("until", None)
+            
+            context_tokens = self.tok_encode(context)
+            
+            all_tokens = []
+            state = None
+            
+            out, state = model.forward(context_tokens, state)
+
+            for i in range(self.max_gen_toks):
+                token = torch.argmax(out).item()
+                
+                if until and any(self.tok_decode([token]).startswith(stop_str) for stop_str in until):
+                    break
+                if token in STOP_TOKEN:
+                    break
+
+                all_tokens.append(token)
+                out, state = model.forward([token], state)
+
+            res.append(self.tok_decode(all_tokens))
+        return res
+
+adapter = EvalHarnessAdapter()
+
+print(f'Running evaluation on {eval_tasks} with {num_fewshot}-shot examples')
+results = simple_evaluate(
+    model=adapter,
+    tasks=eval_tasks,
+    num_fewshot=num_fewshot,
+    limit=None,
+    bootstrap_iters=10000,
+)
+
+print(json.dumps(results['results'], indent=2))
+
+task_str = '-'.join(eval_tasks)
+output_dir = os.path.dirname(MODEL_NAME)
+if not output_dir:
+    output_dir = "."
+base_name = os.path.basename(MODEL_NAME)
+metric_output_path = os.path.join(output_dir, f"{base_name.replace('.pth', '')}_{task_str}.json")
+
+output_dict = dict(model=MODEL_NAME, tasks=eval_tasks, num_fewshot=num_fewshot, results=results['results'])
+with open(metric_output_path, 'w', encoding='utf-8') as f:
+    json.dump(output_dict, f, indent=2, ensure_ascii=False)
+
+print(f"Results saved to {metric_output_path}")