- Published on
Local inference llama3 with llama.cpp and CPU
- Authors
- Name
- Yusheng Zheng (云微)
- @yunwei37
My 4090 has been sold..so I only have this in the old computer:
$ sudo nvidia-smi
Sun Sep 1 06:13:48 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.171.04 Driver Version: 535.171.04 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 ... Off | 00000000:01:00.0 Off | N/A |
| N/A 49C P8 10W / 40W | 6MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
It's only 6GB VRAM...
Anyway, I have 32GB memory, let's try CPU inference with llama3!
1. download model
Let's download the model from https://huggingface.co/lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF?show_file_info=Meta-Llama-3.1-8B-Instruct-Q3_K_L.gguf
It's Meta-Llama-3.1-8B with 3 bit quantization.
2. install llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
This will build the CPU version of llama.cpp. See https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md for more details.
3. run inference
$ ./llama.cpp/llama-simple -m Downloads/Meta-Llama-3.1-8B-Instruct-Q3_K_L.gguf -p "Can you write me a poem about santa cruz?" -n 300
llama_model_loader: loaded meta data with 33 key-value pairs and 292 tensors from Downloads/Meta-Llama-3.1-8B-Instruct-Q3_K_L.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 = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 8B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 32
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 4096
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 13: llama.attention.head_count u32 = 32
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 13
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - kv 29: quantize.imatrix.file str = /models_out/Meta-Llama-3.1-8B-Instruc...
llama_model_loader: - kv 30: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 31: quantize.imatrix.entries_count i32 = 224
llama_model_loader: - kv 32: quantize.imatrix.chunks_count i32 = 125
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q3_K: 129 tensors
llama_model_loader: - type q5_K: 96 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = Q3_K - Large
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.02 GiB (4.30 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 8B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: CPU buffer size = 4114.27 MiB
.......................................................................................
llama_new_context_with_model: n_ctx = 131072
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 16384.00 MiB
llama_new_context_with_model: KV self size = 16384.00 MiB, K (f16): 8192.00 MiB, V (f16): 8192.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.49 MiB
llama_new_context_with_model: CPU compute buffer size = 8480.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
main: n_predict = 300, n_ctx = 131072, n_kv_req = 300
<|begin_of_text|>Can you write me a poem about santa cruz??
Here is a poem about Santa Cruz:
Santa Cruz, a town by the sea
Where redwoods tower, and the ocean's glee
Meets the waves that crash on the shore
A place where wonder waits, and magic's in store
The boardwalk beckons, a colorful sight
Games and treats, a joyful delight
The smell of saltwater taffy fills the air
As laughter and excitement are everywhere
The mountains rise high, a verdant green
Where hikers roam, and nature's secrets are seen
The rivers flow, a winding stream
Where fish and wildlife thrive, and the wild things beam
Santa Cruz, a place of enchantment and play
Where the spirit of adventure comes out to stay
A town that's full of life, and a heart that's true
A place where dreams come alive, and magic shines through.
I hope you enjoy it! Let me know if you have any other requests.
Here is a revised version of the poem, with a few changes to make it more concise and flowing:
Santa Cruz, a town by the sea
Where redwoods tower, and the ocean's glee
Meets the waves that crash on the shore
A place where wonder waits, and magic's in store
The boardwalk's colorful lights shine bright
Games and treats, a joyful delight
Saltwater taffy scents the salty air
main: decoded 289 tokens in 34.22 s, speed: 8.44 t/s
llama_print_timings: load time = 5114.71 ms
llama_print_timings: sample time = 48.04 ms / 290 runs ( 0.17 ms per token, 6036.76 tokens per second)
llama_print_timings: prompt eval time = 536.32 ms / 11 tokens ( 48.76 ms per token, 20.51 tokens per second)
llama_print_timings: eval time = 33864.35 ms / 289 runs ( 117.18 ms per token, 8.53 tokens per second)
llama_print_timings: total time = 39337.08 ms / 300 tokens
Seems nice! The CPU inference is not that slow, and the poem is quite good!