Instructions to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="legraphista/Llama-3.2-3B-Instruct-IMat-GGUF",
	filename="Llama-3.2-3B-Instruct.BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf legraphista/Llama-3.2-3B-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-server -hf legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf legraphista/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf legraphista/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S

Use Docker

docker model run hf.co/legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use legraphista/Llama-3.2-3B-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/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-IMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S

Ollama
How to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with Ollama:
```
ollama run hf.co/legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S
```

Unsloth Studio

How to use legraphista/Llama-3.2-3B-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/Llama-3.2-3B-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/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-IMat-GGUF to start chatting

How to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf legraphista/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use legraphista/Llama-3.2-3B-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-server -hf legraphista/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S

Run Hermes

hermes

Docker Model Runner
How to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with Docker Model Runner:
```
docker model run hf.co/legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S
```

Lemonade

How to use legraphista/Llama-3.2-3B-Instruct-IMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull legraphista/Llama-3.2-3B-Instruct-IMat-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Llama-3.2-3B-Instruct-IMat-GGUF-Q4_K_S

List all available models

lemonade list

Llama-3.2-3B-Instruct-IMat-GGUF

File size: 10,129 Bytes

1e41e54

build: 3825 (1e436302) with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
llama_model_loader: loaded meta data with 31 key-value pairs and 255 tensors from Llama-3.2-3B-Instruct-IMat-GGUF/Llama-3.2-3B-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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 3B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Llama-3.2
llama_model_loader: - kv   5:                         general.size_label str              = 3B
llama_model_loader: - kv   6:                            general.license str              = llama3.2
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              = 28
llama_model_loader: - kv  10:                       llama.context_length u32              = 131072
llama_model_loader: - kv  11:                     llama.embedding_length u32              = 3072
llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 8192
llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 24
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:                 llama.attention.key_length u32              = 128
llama_model_loader: - kv  18:               llama.attention.value_length u32              = 128
llama_model_loader: - kv  19:                          general.file_type u32              = 7
llama_model_loader: - kv  20:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  21:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  22:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  23:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  24:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  25:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  26:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  27:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  29:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv  30:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   58 tensors
llama_model_loader: - type q8_0:  197 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           = 3072
llm_load_print_meta: n_layer          = 28
llm_load_print_meta: n_head           = 24
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            = 3
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  = 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       = ?B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 3.21 B
llm_load_print_meta: model size       = 3.18 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = Llama 3.2 3B 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: EOM token        = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token        = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token        = 128009 '<|eot_id|>'
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 =    0.24 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 =   399.23 MiB
llm_load_tensors:      CUDA0 buffer size =  3255.91 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  = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =    56.00 MiB
llama_new_context_with_model: KV self size  =   56.00 MiB, K (f16):   28.00 MiB, V (f16):   28.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.49 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   256.50 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     7.01 MiB
llama_new_context_with_model: graph nodes  = 902
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 41.918 ms
compute_imatrix: computing over 125 chunks with batch_size 512
compute_imatrix: 0.42 seconds per pass - ETA 0.87 minutes
[1]7.2119,[2]6.1829,[3]5.5587,[4]7.1510,[5]7.5023,[6]6.2757,[7]6.8649,[8]7.4348,[9]7.5568,[10]6.8180,[11]7.3905,[12]8.1158,[13]8.7121,[14]9.2089,[15]9.5275,[16]9.8991,[17]10.1629,[18]9.7576,[19]9.2350,[20]9.1956,[21]9.4054,[22]9.3676,[23]9.7558,[24]9.8068,[25]10.1746,[26]10.2128,[27]10.4085,[28]10.8121,[29]10.8333,[30]10.8456,[31]10.2018,[32]9.6355,[33]9.3259,[34]9.0651,[35]9.2008,[36]9.4165,[37]9.3327,[38]9.3945,[39]9.6091,[40]9.7163,[41]10.0393,[42]10.3529,[43]10.7471,[44]10.9938,[45]11.3462,[46]11.1015,[47]11.2469,[48]11.3189,[49]11.4312,[50]11.2293,[51]11.3600,[52]11.5344,[53]11.6684,[54]11.8114,[55]11.8667,[56]11.8661,[57]11.9075,[58]11.8854,[59]11.8940,[60]11.7947,[61]11.7427,[62]11.7873,[63]11.8043,[64]11.6918,[65]11.6620,[66]11.6606,[67]11.5863,[68]11.5385,[69]11.4857,[70]11.4535,[71]11.4153,[72]11.3756,[73]11.3061,[74]11.2030,[75]11.1858,[76]11.1992,[77]11.1501,[78]11.1214,[79]11.1589,[80]11.1833,[81]11.1465,[82]11.1473,[83]11.1596,[84]10.9894,[85]11.0081,[86]11.0141,[87]10.9985,[88]11.0211,[89]11.0068,[90]10.9066,[91]10.7907,[92]10.6795,[93]10.5833,[94]10.4785,[95]10.3893,[96]10.3235,[97]10.3154,[98]10.3446,[99]10.4593,[100]10.5527,[101]10.6176,[102]10.7876,[103]10.8245,[104]10.8644,[105]10.7447,[106]10.7327,[107]10.6605,[108]10.6070,[109]10.5245,[110]10.5766,[111]10.6507,[112]10.6395,[113]10.6415,[114]10.6915,[115]10.7430,[116]10.7440,[117]10.7599,[118]10.7841,[119]10.6990,[120]10.7534,[121]10.8431,[122]10.8934,[123]10.9826,[124]11.0632,[125]11.1480,
Final estimate: PPL = 11.1480 +/- 0.17302

llama_perf_context_print:        load time =    1375.36 ms
llama_perf_context_print: prompt eval time =   38403.22 ms / 64000 tokens (    0.60 ms per token,  1666.53 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 =   40237.27 ms / 64001 tokens