Instructions to use MatclassAI/Matclass-Gemma-2b-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatclassAI/Matclass-Gemma-2b-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MatclassAI/Matclass-Gemma-2b-v1-GGUF",
	filename="gemma-2-2b-it.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MatclassAI/Matclass-Gemma-2b-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M

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 MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M

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 MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use MatclassAI/Matclass-Gemma-2b-v1-GGUF with Ollama:
```
ollama run hf.co/MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use MatclassAI/Matclass-Gemma-2b-v1-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 MatclassAI/Matclass-Gemma-2b-v1-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 MatclassAI/Matclass-Gemma-2b-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MatclassAI/Matclass-Gemma-2b-v1-GGUF to start chatting

Docker Model Runner
How to use MatclassAI/Matclass-Gemma-2b-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M
```

Lemonade

How to use MatclassAI/Matclass-Gemma-2b-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MatclassAI/Matclass-Gemma-2b-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Matclass-Gemma-2b-v1-GGUF-Q4_K_M

List all available models

lemonade list

Matclass-Gemma-2b-v1-GGUF

File size: 1,789 Bytes

{
    "architectures": [
        "Gemma2ForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "attn_logit_softcapping": 50.0,
    "bos_token_id": 2,
    "cache_implementation": "hybrid",
    "torch_dtype": "float16",
    "eos_token_id": [
        1,
        107
    ],
    "final_logit_softcapping": 30.0,
    "head_dim": 256,
    "hidden_act": "gelu_pytorch_tanh",
    "hidden_activation": "gelu_pytorch_tanh",
    "hidden_size": 2304,
    "initializer_range": 0.02,
    "intermediate_size": 9216,
    "layer_types": [
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention",
        "sliding_attention",
        "full_attention"
    ],
    "max_position_embeddings": 8192,
    "model_type": "gemma2",
    "num_attention_heads": 8,
    "num_hidden_layers": 26,
    "num_key_value_heads": 4,
    "pad_token_id": 0,
    "query_pre_attn_scalar": 256,
    "rms_norm_eps": 1e-06,
    "rope_parameters": {
        "rope_theta": 10000.0,
        "rope_type": "default"
    },
    "sliding_window": 4096,
    "tie_word_embeddings": true,
    "unsloth_version": "2026.6.1",
    "use_bidirectional_attention": null,
    "use_cache": false,
    "vocab_size": 256000
}