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Description
Name and Version
./llama-cli -m granite-3.1-3b-a800m-instruct-Q4_0.gguf -p "What is the capital of France? Answer consisely" -ngl 90
built using the adrenoCL tools.
Ran on samsung s25 with snapdragon 8 elite
version: 6166 (c36e54d06)
built with Android (11349228, +pgo, +bolt, +lto, -mlgo, based on r487747e) clang version 17.0.2 (https://android.googlesource.com/toolchain/llvm-project d9f89f4d16663d5012e5c09495f3b30ece3d2362) for x86_64-unknown-linux-gnu
Operating systems
Linux
GGML backends
OpenCL
Hardware
samsung s25 with snapdragon 8 elite
Models
granite-3.1-3b-a800m-instruct-Q4_0.gguf
Problem description & steps to reproduce
When I run the granite ibm Q4 moe model on the latest release of llama.cpp I get garbled output. When I revert back to the version before mxfp4 I get more coherent output, but I lose the ability to use Q8 MoE models.
First Bad Commit
Relevant log output
130|pa1q:/data/local/tmp # ./llama-cli -m granite-3.1-3b-a800m-instruct-Q4_0.gguf -p "What is the capital of France? Answer consisely" -ngl 90
ggml_opencl: selected platform: 'QUALCOMM Snapdragon(TM)'
ggml_opencl: device: 'QUALCOMM Adreno(TM) 830 (OpenCL 3.0 Adreno(TM) 830)'
ggml_opencl: OpenCL driver: OpenCL 3.0 QUALCOMM build: commit unknown Compiler E031.47.18.13
ggml_opencl: vector subgroup broadcast support: true
ggml_opencl: device FP16 support: true
ggml_opencl: mem base addr align: 128
ggml_opencl: max mem alloc size: 1024 MB
ggml_opencl: SVM coarse grain buffer support: true
ggml_opencl: SVM fine grain buffer support: true
ggml_opencl: SVM fine grain system support: false
ggml_opencl: SVM atomics support: true
ggml_opencl: flattening quantized weights representation as struct of arrays (GGML_OPENCL_SOA_Q)
ggml_opencl: using kernels optimized for Adreno (GGML_OPENCL_USE_ADRENO_KERNELS)
ggml_opencl: loading OpenCL kernels..................................................................
ggml_opencl: default device: 'QUALCOMM Adreno(TM) 830 (OpenCL 3.0 Adreno(TM) 830)'
build: 6166 (c36e54d06) with Android (11349228, +pgo, +bolt, +lto, -mlgo, based on r487747e) clang version 17.0.2 (https://android.googlesource.com/toolchain/llvm-project d9f89f4d16663d5012e5c09495f3b30ece3d2362) for x86_64-unknown-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device GPUOpenCL (QUALCOMM Adreno(TM) 830) - 0 MiB free
llama_model_loader: loaded meta data with 46 key-value pairs and 322 tensors from granite-3.1-3b-a800m-instruct-Q4_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = granitemoe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Granite 3.1 3b A800M Instruct
llama_model_loader: - kv 3: general.finetune str = instruct
llama_model_loader: - kv 4: general.basename str = granite-3.1
llama_model_loader: - kv 5: general.size_label str = 3B-a800M
llama_model_loader: - kv 6: general.license str = apache-2.0
llama_model_loader: - kv 7: general.base_model.count u32 = 1
llama_model_loader: - kv 8: general.base_model.0.name str = Granite 3.1 3b A800M Base
llama_model_loader: - kv 9: general.base_model.0.organization str = Ibm Granite
llama_model_loader: - kv 10: general.base_model.0.repo_url str = https://huggingface.co/ibm-granite/gr...
llama_model_loader: - kv 11: general.tags arr[str,3] = ["language", "granite-3.1", "text-gen...
llama_model_loader: - kv 12: granitemoe.block_count u32 = 32
llama_model_loader: - kv 13: granitemoe.context_length u32 = 131072
llama_model_loader: - kv 14: granitemoe.embedding_length u32 = 1536
llama_model_loader: - kv 15: granitemoe.feed_forward_length u32 = 512
llama_model_loader: - kv 16: granitemoe.attention.head_count u32 = 24
llama_model_loader: - kv 17: granitemoe.attention.head_count_kv u32 = 8
llama_model_loader: - kv 18: granitemoe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 19: granitemoe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 20: granitemoe.expert_count u32 = 40
llama_model_loader: - kv 21: granitemoe.expert_used_count u32 = 8
llama_model_loader: - kv 22: general.file_type u32 = 2
llama_model_loader: - kv 23: granitemoe.vocab_size u32 = 49155
llama_model_loader: - kv 24: granitemoe.rope.dimension_count u32 = 64
llama_model_loader: - kv 25: granitemoe.attention.scale f32 = 0.015625
llama_model_loader: - kv 26: granitemoe.embedding_scale f32 = 12.000000
llama_model_loader: - kv 27: granitemoe.residual_scale f32 = 0.220000
llama_model_loader: - kv 28: granitemoe.logit_scale f32 = 6.000000
llama_model_loader: - kv 29: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 30: tokenizer.ggml.pre str = refact
llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,49155] = ["<|end_of_text|>", "<fim_prefix>", "...
llama_model_loader: - kv 32: tokenizer.ggml.token_type arr[i32,49155] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 33: tokenizer.ggml.merges arr[str,48891] = ["Ġ Ġ", "ĠĠ ĠĠ", "ĠĠĠĠ ĠĠ...
llama_model_loader: - kv 34: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 35: tokenizer.ggml.eos_token_id u32 = 0
llama_model_loader: - kv 36: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 37: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 38: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 39: tokenizer.chat_template str = {%- if messages[0]['role'] == 'system...
llama_model_loader: - kv 40: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 41: general.quantization_version u32 = 2
llama_model_loader: - kv 42: quantize.imatrix.file str = /models_out/granite-3.1-3b-a800m-inst...
llama_model_loader: - kv 43: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 44: quantize.imatrix.entries_count i32 = 250
llama_model_loader: - kv 45: quantize.imatrix.chunks_count i32 = 152
llama_model_loader: - type f32: 97 tensors
llama_model_loader: - type q4_0: 220 tensors
llama_model_loader: - type q4_1: 4 tensors
llama_model_loader: - type q6_K: 1 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_0
print_info: file size = 1.76 GiB (4.58 BPW)
load: printing all EOG tokens:
load: - 0 ('<|end_of_text|>')
load: - 4 ('<fim_pad>')
load: - 18 ('<reponame>')
load: special tokens cache size = 22
load: token to piece cache size = 0.2826 MB
print_info: arch = granitemoe
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 1536
print_info: n_layer = 32
print_info: n_head = 24
print_info: n_head_kv = 8
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 64
print_info: n_embd_head_v = 64
print_info: n_gqa = 3
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
n_tensors: 322
print_info: f_logit_scale = 6.0e+00
Context size 142416
print_info: f_attn_scale = 1.6e-02
print_info: n_ff = 512
print_info: n_expert = 40
print_info: n_expert_used = 8
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 131072
print_info: rope_finetuned = yes
print_info: model type = 3B
print_info: model params = 3.30 B
print_info: general.name = Granite 3.1 3b A800M Instruct
print_info: f_embedding_scale = 12.000000
print_info: f_residual_scale = 0.220000
print_info: f_attention_scale = 0.015625
print_info: n_ff_shexp = 0
print_info: vocab type = BPE
print_info: n_vocab = 49155
print_info: n_merges = 48891
print_info: BOS token = 0 '<|end_of_text|>'
print_info: EOS token = 0 '<|end_of_text|>'
print_info: UNK token = 0 '<|end_of_text|>'
print_info: PAD token = 0 '<|end_of_text|>'
print_info: LF token = 203 'Ċ'
print_info: FIM PRE token = 1 '<fim_prefix>'
print_info: FIM SUF token = 3 '<fim_suffix>'
print_info: FIM MID token = 2 '<fim_middle>'
print_info: FIM PAD token = 4 '<fim_pad>'
print_info: FIM REP token = 18 '<reponame>'
print_info: EOG token = 0 '<|end_of_text|>'
print_info: EOG token = 4 '<fim_pad>'
print_info: EOG token = 18 '<reponame>'
print_info: max token length = 512
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 32 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 33/33 layers to GPU
load_tensors: CPU_Mapped model buffer size = 249.56 MiB
load_tensors: OpenCL model buffer size = 1727.45 MiB
................................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 0
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context: CPU output buffer size = 0.19 MiB
llama_kv_cache_unified: OpenCL KV buffer size = 256.00 MiB
llama_kv_cache_unified: size = 256.00 MiB ( 4096 cells, 32 layers, 1/1 seqs), K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_context: OpenCL compute buffer size = 235.16 MiB
llama_context: CPU compute buffer size = 34.01 MiB
llama_context: graph nodes = 2056
llama_context: graph splits = 10
common_init_from_params: added <|end_of_text|> logit bias = -inf
common_init_from_params: added <fim_pad> logit bias = -inf
common_init_from_params: added <reponame> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 8
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
*** User-specified prompt will pre-start conversation, did you mean to set --system-prompt (-sys) instead?
main: chat template example:
<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>
<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>
<|start_of_role|>user<|end_of_role|>How are you?<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>
system_info: n_threads = 8 (n_threads_batch = 8) / 8 | CPU : NEON = 1 | ARM_FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
main: interactive mode on.
sampler seed: 3717627816
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 0
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to the AI.
- To return control without starting a new line, end your input with '/'.
- If you want to submit another line, end your input with '\'.
- Not using system message. To change it, set a different value via -sys PROMPT
userWhat is the capital of France? Answer consisely
assistant
The capital of France is Paris.[PARISON]
Paris
Paris
The capital city of
France
is
Paris.
Indeed
Here's the capital city ofFrance
in
Paris
indeed
>
llama_perf_sampler_print: sampling time = 3.81 ms / 89 runs ( 0.04 ms per token, 23353.45 tokens per second)
llama_perf_context_print: load time = 3893.32 ms
llama_perf_context_print: prompt eval time = 229.87 ms / 20 tokens ( 11.49 ms per token, 87.01 tokens per second)
llama_perf_context_print: eval time = 1562.51 ms / 68 runs ( 22.98 ms per token, 43.52 tokens per second)
llama_perf_context_print: total time = 4077.01 ms / 88 tokens
llama_perf_context_print: graphs reused = 65
Interrupted by user