Instructions to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF",
	filename="Qwen2.5-Math-1.5B-Instruct.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf legraphista/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf legraphista/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf legraphista/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

Use Docker

docker model run hf.co/legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "legraphista/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

Ollama
How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with Ollama:
```
ollama run hf.co/legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S
```

Unsloth Studio

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-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/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF to start chatting

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with Docker Model Runner:
```
docker model run hf.co/legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S
```

Lemonade

How to use legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull legraphista/Qwen2.5-Math-1.5B-Instruct-IMat-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Qwen2.5-Math-1.5B-Instruct-IMat-GGUF-Q4_K_S

List all available models

lemonade list

Qwen2.5-Math-1.5B-Instruct-IMat-GGUF / imatrix.log

legraphista

Upload imatrix.log with huggingface_hub

9270fec verified almost 2 years ago

Raw

History Blame

10.5 kB

	build: 3787 (6026da52) with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
	llama_model_loader: loaded meta data with 34 key-value pairs and 338 tensors from Qwen2.5-Math-1.5B-Instruct-IMat-GGUF/Qwen2.5-Math-1.5B-Instruct.Q8_0.gguf.hardlink.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 = qwen2
	llama_model_loader: - kv 1: general.type str = model
	llama_model_loader: - kv 2: general.name str = Qwen2.5 Math 1.5B Instruct
	llama_model_loader: - kv 3: general.finetune str = Instruct
	llama_model_loader: - kv 4: general.basename str = Qwen2.5-Math
	llama_model_loader: - kv 5: general.size_label str = 1.5B
	llama_model_loader: - kv 6: general.license str = apache-2.0
	llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-M...
	llama_model_loader: - kv 8: general.base_model.count u32 = 1
	llama_model_loader: - kv 9: general.base_model.0.name str = Qwen2.5 Math 1.5B
	llama_model_loader: - kv 10: general.base_model.0.organization str = Qwen
	llama_model_loader: - kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-M...
	llama_model_loader: - kv 12: general.tags arr[str,2] = ["chat", "text-generation"]
	llama_model_loader: - kv 13: general.languages arr[str,1] = ["en"]
	llama_model_loader: - kv 14: qwen2.block_count u32 = 28
	llama_model_loader: - kv 15: qwen2.context_length u32 = 4096
	llama_model_loader: - kv 16: qwen2.embedding_length u32 = 1536
	llama_model_loader: - kv 17: qwen2.feed_forward_length u32 = 8960
	llama_model_loader: - kv 18: qwen2.attention.head_count u32 = 12
	llama_model_loader: - kv 19: qwen2.attention.head_count_kv u32 = 2
	llama_model_loader: - kv 20: qwen2.rope.freq_base f32 = 10000.000000
	llama_model_loader: - kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
	llama_model_loader: - kv 22: general.file_type u32 = 7
	llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
	llama_model_loader: - kv 24: tokenizer.ggml.pre str = qwen2
	llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
	llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
	llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
	llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 151645
	llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 151643
	llama_model_loader: - kv 30: tokenizer.ggml.bos_token_id u32 = 151643
	llama_model_loader: - kv 31: tokenizer.ggml.add_bos_token bool = false
	llama_model_loader: - kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<\|im_start\|>...
	llama_model_loader: - kv 33: general.quantization_version u32 = 2
	llama_model_loader: - type f32: 141 tensors
	llama_model_loader: - type q8_0: 197 tensors
	llm_load_vocab: special tokens cache size = 22
	llm_load_vocab: token to piece cache size = 0.9310 MB
	llm_load_print_meta: format = GGUF V3 (latest)
	llm_load_print_meta: arch = qwen2
	llm_load_print_meta: vocab type = BPE
	llm_load_print_meta: n_vocab = 151936
	llm_load_print_meta: n_merges = 151387
	llm_load_print_meta: vocab_only = 0
	llm_load_print_meta: n_ctx_train = 4096
	llm_load_print_meta: n_embd = 1536
	llm_load_print_meta: n_layer = 28
	llm_load_print_meta: n_head = 12
	llm_load_print_meta: n_head_kv = 2
	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 = 6
	llm_load_print_meta: n_embd_k_gqa = 256
	llm_load_print_meta: n_embd_v_gqa = 256
	llm_load_print_meta: f_norm_eps = 0.0e+00
	llm_load_print_meta: f_norm_rms_eps = 1.0e-06
	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 = 8960
	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 = 2
	llm_load_print_meta: rope scaling = linear
	llm_load_print_meta: freq_base_train = 10000.0
	llm_load_print_meta: freq_scale_train = 1
	llm_load_print_meta: n_ctx_orig_yarn = 4096
	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 = ?B
	llm_load_print_meta: model ftype = Q8_0
	llm_load_print_meta: model params = 1.54 B
	llm_load_print_meta: model size = 1.53 GiB (8.50 BPW)
	llm_load_print_meta: general.name = Qwen2.5 Math 1.5B Instruct
	llm_load_print_meta: BOS token = 151643 '<\|endoftext\|>'
	llm_load_print_meta: EOS token = 151645 '<\|im_end\|>'
	llm_load_print_meta: PAD token = 151643 '<\|endoftext\|>'
	llm_load_print_meta: LF token = 148848 'ÄĬ'
	llm_load_print_meta: EOT token = 151645 '<\|im_end\|>'
	llm_load_print_meta: max token length = 256
	ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
	ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
	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 = 0.30 MiB
	llm_load_tensors: offloading 28 repeating layers to GPU
	llm_load_tensors: offloading non-repeating layers to GPU
	llm_load_tensors: offloaded 29/29 layers to GPU
	llm_load_tensors: CPU buffer size = 236.47 MiB
	llm_load_tensors: CUDA0 buffer size = 1564.63 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 = 10000.0
	llama_new_context_with_model: freq_scale = 1
	llama_kv_cache_init: CUDA0 KV buffer size = 14.00 MiB
	llama_new_context_with_model: KV self size = 14.00 MiB, K (f16): 7.00 MiB, V (f16): 7.00 MiB
	llama_new_context_with_model: CUDA_Host output buffer size = 0.58 MiB
	llama_new_context_with_model: CUDA0 compute buffer size = 299.75 MiB
	llama_new_context_with_model: CUDA_Host compute buffer size = 4.01 MiB
	llama_new_context_with_model: graph nodes = 986
	llama_new_context_with_model: graph splits = 2

	system_info: n_threads = 25 (n_threads_batch = 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 \| RISCV_VECT = 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 135.209 ms
	compute_imatrix: computing over 128 chunks with batch_size 512
	compute_imatrix: 0.39 seconds per pass - ETA 0.82 minutes
	[1]76.3534,[2]44.0303,[3]31.6350,[4]37.0097,[5]36.9772,[6]40.0268,[7]38.6530,[8]39.8474,[9]41.0920,[10]39.7125,[11]35.6288,[12]37.8969,[13]41.1135,[14]41.3761,[15]45.6199,[16]46.1514,[17]47.9945,[18]51.2402,[19]50.3270,[20]49.3483,[21]58.2344,[22]62.7025,[23]62.9567,[24]64.2388,[25]63.9855,[26]65.5560,[27]68.5196,[28]71.2349,[29]73.9642,[30]78.4475,[31]83.2389,[32]83.4088,[33]79.8979,[34]76.4622,[35]72.9805,[36]82.5251,[37]94.8080,[38]101.6563,[39]101.6932,[40]102.5662,[41]102.5259,[42]105.8845,[43]107.2122,[44]108.9494,[45]110.5805,[46]111.8862,[47]109.9830,[48]107.9592,[49]106.6537,[50]105.1476,[51]104.0614,[52]103.1540,[53]106.6089,[54]106.5142,[55]108.6973,[56]108.7682,[57]107.6624,[58]106.6510,[59]105.5954,[60]105.6071,[61]104.0959,[62]103.6760,[63]104.2393,[64]105.8032,[65]105.3703,[66]103.8000,[67]102.4559,[68]100.5003,[69]100.1043,[70]98.5139,[71]96.2124,[72]94.4408,[73]93.1674,[74]90.8032,[75]88.3730,[76]86.1229,[77]84.7100,[78]83.7798,[79]83.0234,[80]81.6884,[81]81.0501,[82]80.2872,[83]79.1985,[84]79.5808,[85]79.0158,[86]80.0719,[87]79.2331,[88]78.5611,[89]78.4093,[90]78.2067,[91]77.6363,[92]75.3036,[93]73.1595,[94]70.9507,[95]68.8827,[96]66.9942,[97]65.1730,[98]63.3927,[99]64.1802,[100]64.3003,[101]64.0369,[102]64.5606,[103]64.9922,[104]65.3317,[105]66.1201,[106]66.9926,[107]67.1652,[108]66.6607,[109]66.7239,[110]66.8040,[111]65.5796,[112]64.5900,[113]64.3368,[114]64.3996,[115]64.6678,[116]64.7223,[117]65.0348,[118]65.3331,[119]65.3733,[120]65.2025,[121]65.0798,[122]64.4611,[123]64.6898,[124]65.0316,[125]65.5398,[126]66.2413,[127]66.7434,[128]67.0922,
	Final estimate: PPL = 67.0922 +/- 1.51836

	llama_perf_context_print: load time = 1153.45 ms
	llama_perf_context_print: prompt eval time = 30925.78 ms / 65536 tokens ( 0.47 ms per token, 2119.14 tokens per second)
	llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
	llama_perf_context_print: total time = 32688.54 ms / 65537 tokens