Instructions to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

Use Docker

docker model run hf.co/legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "legraphista/internlm2-math-plus-1_8b-IMat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "legraphista/internlm2-math-plus-1_8b-IMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

Ollama
How to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with Ollama:
```
ollama run hf.co/legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S
```

Unsloth Studio

How to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for legraphista/internlm2-math-plus-1_8b-IMat-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for legraphista/internlm2-math-plus-1_8b-IMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for legraphista/internlm2-math-plus-1_8b-IMat-GGUF to start chatting

Docker Model Runner
How to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with Docker Model Runner:
```
docker model run hf.co/legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S
```

Lemonade

How to use legraphista/internlm2-math-plus-1_8b-IMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull legraphista/internlm2-math-plus-1_8b-IMat-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.internlm2-math-plus-1_8b-IMat-GGUF-Q4_K_S

List all available models

lemonade list

Atomic Chat

legraphista commited on May 27, 2024

Commit

94e9f67

verified ·

1 Parent(s): 3a4b4dd

Upload imatrix.log with huggingface_hub

Browse files

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imatrix.log +155 -0

imatrix.log ADDED Viewed

	@@ -0,0 +1,155 @@

+main: build = 3008 (1d8fca72)
+main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
+main: seed  = 1716817514
+llama_model_loader: loaded meta data with 22 key-value pairs and 219 tensors from internlm2-math-plus-1_8b-IMat-GGUF/internlm2-math-plus-1_8b.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              = internlm2
+llama_model_loader: - kv   1:                               general.name str              = InternLM2
+llama_model_loader: - kv   2:                   internlm2.context_length u32              = 8192
+llama_model_loader: - kv   3:                      internlm2.block_count u32              = 24
+llama_model_loader: - kv   4:                 internlm2.embedding_length u32              = 2048
+llama_model_loader: - kv   5:              internlm2.feed_forward_length u32              = 8192
+llama_model_loader: - kv   6:                   internlm2.rope.freq_base f32              = 1000000.000000
+llama_model_loader: - kv   7:             internlm2.attention.head_count u32              = 16
+llama_model_loader: - kv   8: internlm2.attention.layer_norm_rms_epsilon f32              = 0.000010
+llama_model_loader: - kv   9:          internlm2.attention.head_count_kv u32              = 8
+llama_model_loader: - kv  10:                          general.file_type u32              = 0
+llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama
+llama_model_loader: - kv  12:                         tokenizer.ggml.pre str              = default
+llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,92544]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
+llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,92544]   = [0.000000, 0.000000, 0.000000, 0.0000...
+llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,92544]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
+llama_model_loader: - kv  16:            tokenizer.ggml.add_space_prefix bool             = false
+llama_model_loader: - kv  17:                tokenizer.ggml.bos_token_id u32              = 1
+llama_model_loader: - kv  18:                tokenizer.ggml.eos_token_id u32              = 2
+llama_model_loader: - kv  19:            tokenizer.ggml.padding_token_id u32              = 2
+llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
+llama_model_loader: - kv  21:               general.quantization_version u32              = 2
+llama_model_loader: - type  f32:  219 tensors
+llm_load_vocab: mismatch in special tokens definition ( 405/92544 vs 259/92544 ).
+llm_load_print_meta: format           = GGUF V3 (latest)
+llm_load_print_meta: arch             = internlm2
+llm_load_print_meta: vocab type       = SPM
+llm_load_print_meta: n_vocab          = 92544
+llm_load_print_meta: n_merges         = 0
+llm_load_print_meta: n_ctx_train      = 8192
+llm_load_print_meta: n_embd           = 2048
+llm_load_print_meta: n_head           = 16
+llm_load_print_meta: n_head_kv        = 8
+llm_load_print_meta: n_layer          = 24
+llm_load_print_meta: n_rot            = 128
+llm_load_print_meta: n_embd_head_k    = 128
+llm_load_print_meta: n_embd_head_v    = 128
+llm_load_print_meta: n_gqa            = 2
+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             = 8192
+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  = 1000000.0
+llm_load_print_meta: freq_scale_train = 1
+llm_load_print_meta: n_yarn_orig_ctx  = 8192
+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: model type       = ?B
+llm_load_print_meta: model ftype      = all F32
+llm_load_print_meta: model params     = 1.89 B
+llm_load_print_meta: model size       = 7.04 GiB (32.00 BPW)
+llm_load_print_meta: general.name     = InternLM2
+llm_load_print_meta: BOS token        = 1 '<s>'
+llm_load_print_meta: EOS token        = 2 '</s>'
+llm_load_print_meta: UNK token        = 0 '<unk>'
+llm_load_print_meta: PAD token        = 2 '</s>'
+llm_load_print_meta: LF token         = 13 '<0x0A>'
+ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
+ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
+ggml_cuda_init: found 1 CUDA devices:
+  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
+llm_load_tensors: ggml ctx size =    0.22 MiB
+llm_load_tensors: offloading 24 repeating layers to GPU
+llm_load_tensors: offloading non-repeating layers to GPU
+llm_load_tensors: offloaded 25/25 layers to GPU
+llm_load_tensors:        CPU buffer size =   723.00 MiB
+llm_load_tensors:      CUDA0 buffer size =  6483.38 MiB
+..................................................................................
+llama_new_context_with_model: n_ctx      = 512
+llama_new_context_with_model: n_batch    = 512
+llama_new_context_with_model: n_ubatch   = 512
+llama_new_context_with_model: flash_attn = 0
+llama_new_context_with_model: freq_base  = 1000000.0
+llama_new_context_with_model: freq_scale = 1
+llama_kv_cache_init:      CUDA0 KV buffer size =    48.00 MiB
+llama_new_context_with_model: KV self size  =   48.00 MiB, K (f16):   24.00 MiB, V (f16):   24.00 MiB
+llama_new_context_with_model:  CUDA_Host  output buffer size =     0.35 MiB
+llama_new_context_with_model:      CUDA0 compute buffer size =   184.75 MiB
+llama_new_context_with_model:  CUDA_Host compute buffer size =     5.01 MiB
+llama_new_context_with_model: graph nodes  = 774
+llama_new_context_with_model: graph splits = 2
+system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
+compute_imatrix: tokenizing the input ..
+compute_imatrix: tokenization took 149.089 ms
+compute_imatrix: computing over 209 chunks with batch_size 512
+compute_imatrix: 0.25 seconds per pass - ETA 0.87 minutes
+[1]11.1312,[2]8.6363,[3]7.5192,[4]8.7685,[5]8.6457,[6]8.0250,[7]9.1250,[8]9.1274,[9]9.9765,
+save_imatrix: stored collected data after 10 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[10]10.3272,[11]11.1727,[12]11.3352,[13]12.9598,[14]13.4077,[15]14.4618,[16]15.1761,[17]15.8490,[18]14.8805,[19]15.3401,
+save_imatrix: stored collected data after 20 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[20]15.2807,[21]14.5097,[22]14.4950,[23]13.4617,[24]13.0109,[25]12.2200,[26]12.2099,[27]12.7869,[28]12.7652,[29]13.2829,
+save_imatrix: stored collected data after 30 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[30]13.7325,[31]13.6520,[32]12.9249,[33]12.4811,[34]12.2812,[35]12.2370,[36]12.0979,[37]12.4393,[38]12.8247,[39]13.0730,
+save_imatrix: stored collected data after 40 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[40]13.4472,[41]13.7132,[42]14.1578,[43]14.5769,[44]15.0185,[45]15.1708,[46]15.2992,[47]15.2611,[48]15.1091,[49]15.4024,
+save_imatrix: stored collected data after 50 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[50]15.7380,[51]15.8763,[52]16.2449,[53]16.2822,[54]16.5116,[55]16.7323,[56]17.0042,[57]17.1444,[58]17.3613,[59]17.3927,
+save_imatrix: stored collected data after 60 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[60]17.3337,[61]17.6352,[62]17.9523,[63]18.4370,[64]18.3717,[65]18.1018,[66]17.8581,[67]17.6437,[68]17.4301,[69]17.1963,
+save_imatrix: stored collected data after 70 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[70]17.0333,[71]16.9842,[72]16.7208,[73]16.3945,[74]16.5737,[75]16.7233,[76]16.7770,[77]16.7379,[78]16.9561,[79]17.0106,
+save_imatrix: stored collected data after 80 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[80]17.1238,[81]17.1064,[82]17.1041,[83]17.2104,[84]17.2325,[85]17.2776,[86]17.2832,[87]17.3172,[88]17.2650,[89]17.3473,
+save_imatrix: stored collected data after 90 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[90]17.4284,[91]17.5131,[92]17.4812,[93]17.3928,[94]17.2928,[95]17.2508,[96]17.1120,[97]17.1103,[98]17.0537,[99]16.9868,
+save_imatrix: stored collected data after 100 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[100]16.8327,[101]16.8049,[102]16.7018,[103]16.5576,[104]16.4292,[105]16.3660,[106]16.2770,[107]16.1450,[108]16.0753,[109]16.0705,
+save_imatrix: stored collected data after 110 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[110]16.0731,[111]15.9900,[112]15.9993,[113]16.0081,[114]15.9285,[115]15.8458,[116]15.9694,[117]15.9543,[118]15.9839,[119]15.8023,
+save_imatrix: stored collected data after 120 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[120]15.6437,[121]15.4694,[122]15.2755,[123]15.1011,[124]14.9437,[125]14.7883,[126]14.7195,[127]14.6397,[128]14.5264,[129]14.4086,
+save_imatrix: stored collected data after 130 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[130]14.3366,[131]14.2389,[132]14.1439,[133]14.0853,[134]13.9819,[135]13.8918,[136]13.8522,[137]13.7711,[138]13.6823,[139]13.6393,
+save_imatrix: stored collected data after 140 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[140]13.5538,[141]13.4733,[142]13.5984,[143]13.8155,[144]14.0751,[145]14.2823,[146]14.3138,[147]14.3540,[148]14.4225,[149]14.5055,
+save_imatrix: stored collected data after 150 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[150]14.5680,[151]14.5877,[152]14.6063,[153]14.7027,[154]14.7721,[155]14.8630,[156]14.8986,[157]14.9890,[158]15.0815,[159]15.1157,
+save_imatrix: stored collected data after 160 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[160]15.1994,[161]15.2529,[162]15.2996,[163]15.3659,[164]15.4310,[165]15.4594,[166]15.5148,[167]15.5710,[168]15.6024,[169]15.6428,
+save_imatrix: stored collected data after 170 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[170]15.6873,[171]15.7017,[172]15.7554,[173]15.8180,[174]15.8137,[175]15.9375,[176]16.0642,[177]16.2008,[178]16.3725,[179]16.4786,
+save_imatrix: stored collected data after 180 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[180]16.5336,[181]16.4838,[182]16.5160,[183]16.5718,[184]16.6541,[185]16.6811,[186]16.6915,[187]16.7102,[188]16.7611,[189]16.7740,
+save_imatrix: stored collected data after 190 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[190]16.7763,[191]16.8201,[192]16.8665,[193]16.9179,[194]16.8887,[195]16.9311,[196]16.9176,[197]16.9523,[198]16.9722,[199]17.0944,
+save_imatrix: stored collected data after 200 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+[200]16.9780,[201]17.0414,[202]17.0243,[203]17.1840,[204]17.3468,[205]17.4907,[206]17.6111,[207]17.7104,[208]17.6297,[209]17.5631,
+save_imatrix: stored collected data after 209 chunks in internlm2-math-plus-1_8b-IMat-GGUF/imatrix.dat
+llama_print_timings:        load time =    1311.79 ms
+llama_print_timings:      sample time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
+llama_print_timings: prompt eval time =   32872.80 ms / 107008 tokens (    0.31 ms per token,  3255.21 tokens per second)
+llama_print_timings:        eval time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
+llama_print_timings:       total time =   35006.68 ms / 107009 tokens
+Final estimate: PPL = 17.5631 +/- 0.23703