Instructions to use Timbo89/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Timbo89/model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Timbo89/model", filename="granite-4.0-h-micro.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
- llama.cpp
How to use Timbo89/model with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Timbo89/model:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Timbo89/model:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Timbo89/model:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Timbo89/model: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 Timbo89/model:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Timbo89/model: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 Timbo89/model:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Timbo89/model:Q4_K_M
Use Docker
docker model run hf.co/Timbo89/model:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Timbo89/model with Ollama:
ollama run hf.co/Timbo89/model:Q4_K_M
- Unsloth Studio new
How to use Timbo89/model 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 Timbo89/model 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 Timbo89/model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Timbo89/model to start chatting
- Pi new
How to use Timbo89/model with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Timbo89/model:Q4_K_M
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": "Timbo89/model:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Timbo89/model with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Timbo89/model:Q4_K_M
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 Timbo89/model:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Timbo89/model with Docker Model Runner:
docker model run hf.co/Timbo89/model:Q4_K_M
- Lemonade
How to use Timbo89/model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Timbo89/model:Q4_K_M
Run and chat with the model
lemonade run user.model-Q4_K_M
List all available models
lemonade list
Trained with Unsloth - config
Browse files- config.json +91 -0
config.json
ADDED
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{
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"architectures": [
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"GraniteMoeHybridForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attention_multiplier": 0.015625,
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"bos_token_id": 100257,
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"torch_dtype": "float16",
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"embedding_multiplier": 12,
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"eos_token_id": 100257,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.1,
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"intermediate_size": 8192,
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"layer_types": [
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"attention",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"attention",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"attention",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"mamba",
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"attention",
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"mamba",
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"mamba",
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"mamba",
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"mamba"
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],
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"logits_scaling": 8,
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"mamba_chunk_size": 256,
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"mamba_conv_bias": true,
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"mamba_d_conv": 4,
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"mamba_d_head": 64,
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"mamba_d_state": 128,
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"mamba_expand": 2,
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"mamba_n_groups": 1,
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"mamba_n_heads": 64,
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"mamba_proj_bias": false,
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"max_position_embeddings": 131072,
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"model_type": "granitemoehybrid",
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"normalization_function": "rmsnorm",
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"num_attention_heads": 32,
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"num_experts_per_tok": 0,
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"num_hidden_layers": 40,
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"num_key_value_heads": 8,
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"num_local_experts": 0,
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"output_router_logits": false,
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"pad_token_id": 100256,
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"position_embedding_type": "nope",
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"residual_multiplier": 0.22,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000,
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"router_aux_loss_coef": 0.01,
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"shared_intermediate_size": 8192,
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"tie_word_embeddings": true,
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"transformers_version": "4.56.2",
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"unsloth_fixed": true,
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"unsloth_version": "2025.10.7",
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"use_cache": true,
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"vocab_size": 100352
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}
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