Instructions to use josephmayo/Holo-3.1-9B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use josephmayo/Holo-3.1-9B-Coder with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="josephmayo/Holo-3.1-9B-Coder", filename="holo-9b-coder-Q4_K_M.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 josephmayo/Holo-3.1-9B-Coder 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 josephmayo/Holo-3.1-9B-Coder:Q4_K_M # Run inference directly in the terminal: llama cli -hf josephmayo/Holo-3.1-9B-Coder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf josephmayo/Holo-3.1-9B-Coder:Q4_K_M # Run inference directly in the terminal: llama cli -hf josephmayo/Holo-3.1-9B-Coder: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 josephmayo/Holo-3.1-9B-Coder:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf josephmayo/Holo-3.1-9B-Coder: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 josephmayo/Holo-3.1-9B-Coder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf josephmayo/Holo-3.1-9B-Coder:Q4_K_M
Use Docker
docker model run hf.co/josephmayo/Holo-3.1-9B-Coder:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use josephmayo/Holo-3.1-9B-Coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "josephmayo/Holo-3.1-9B-Coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "josephmayo/Holo-3.1-9B-Coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/josephmayo/Holo-3.1-9B-Coder:Q4_K_M
- Ollama
How to use josephmayo/Holo-3.1-9B-Coder with Ollama:
ollama run hf.co/josephmayo/Holo-3.1-9B-Coder:Q4_K_M
- Unsloth Studio
How to use josephmayo/Holo-3.1-9B-Coder 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 josephmayo/Holo-3.1-9B-Coder 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 josephmayo/Holo-3.1-9B-Coder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for josephmayo/Holo-3.1-9B-Coder to start chatting
- Pi
How to use josephmayo/Holo-3.1-9B-Coder with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf josephmayo/Holo-3.1-9B-Coder: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": "josephmayo/Holo-3.1-9B-Coder:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use josephmayo/Holo-3.1-9B-Coder with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf josephmayo/Holo-3.1-9B-Coder: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 josephmayo/Holo-3.1-9B-Coder:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use josephmayo/Holo-3.1-9B-Coder with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf josephmayo/Holo-3.1-9B-Coder:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "josephmayo/Holo-3.1-9B-Coder:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use josephmayo/Holo-3.1-9B-Coder with Docker Model Runner:
docker model run hf.co/josephmayo/Holo-3.1-9B-Coder:Q4_K_M
- Lemonade
How to use josephmayo/Holo-3.1-9B-Coder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull josephmayo/Holo-3.1-9B-Coder:Q4_K_M
Run and chat with the model
lemonade run user.Holo-3.1-9B-Coder-Q4_K_M
List all available models
lemonade list
| license: apache-2.0 | |
| tags: | |
| - code | |
| - holo | |
| - qwen | |
| language: | |
| - en | |
| - code | |
| base_model: Hcompany/Holo-3.1-9B | |
| pipeline_tag: text-generation | |
| # Holo-3.1-9B-Coder | |
| Code-specialized adaptation of [Hcompany/Holo-3.1-9B](https://huggingface.co/Hcompany/Holo-3.1-9B). | |
| ## Evaluation | |
| | Model | HumanEval+ pass@1 | LiveCodeBench v2 pass@1 | | |
| |-------|-------------------|------------------------| | |
| | Holo-3.1-9B (base) | 52.4% | 31.5% | | |
| | Holo-3.1-9B-Coder (this model) | **65.2%** (+12.8) | **37.8%** (+6.3) | | |
| LiveCodeBench evaluated with official [`codegen_metrics`](https://github.com/LiveCodeBench/LiveCodeBench), greedy decoding, 6s timeout. Proof: [`eval/lcb_v2_official.json`](https://huggingface.co/josephmayo/Holo-3.1-9B-Coder/resolve/main/eval/lcb_v2_official.json) | |
| ## Artifacts | |
| | Path | Description | | |
| |------|-------------| | |
| | `adapter/` | LoRA adapter (r=8, alpha=16, q/v targets). | | |
| | `model.safetensors`, `config.json`, tokenizer files | Merged base + adapter model. | | |
| | `holo-9b-coder-Q4_K_M.gguf` | llama.cpp GGUF, Q4_K_M quantization. | | |
| | `holo-9b-coder-Q5_K_M.gguf` | llama.cpp GGUF, Q5_K_M quantization. | | |
| | `holo-9b-coder-Q6_K.gguf` | llama.cpp GGUF, Q6_K quantization. | | |
| ## Quantization details | |
| Quantization was performed with [llama.cpp](https://github.com/ggml-org/llama.cpp) after merging the adapter into the base model. The GGUF files use K-quant mixture schemes. | |
| ## Loading the adapter | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| base = "Hcompany/Holo-3.1-9B" | |
| model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True, torch_dtype="auto", device_map="auto") | |
| model = PeftModel.from_pretrained(model, "josephmayo/Holo-3.1-9B-Coder", subfolder="adapter") | |
| model = model.merge_and_unload() | |
| tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True) | |
| ``` | |
| ## Loading a GGUF with llama.cpp | |
| ```bash | |
| ./llama-cli -m holo-9b-coder-Q4_K_M.gguf -p "Return only executable Python code.\n\ndef factorial(n):" -n 256 | |
| ``` | |
| ## Base model | |
| - **Architecture:** Qwen3.5-family text backbone (decoder-only transformer). | |
| - **License:** Apache-2.0. | |
| - **Original model:** [Hcompany/Holo-3.1-9B](https://huggingface.co/Hcompany/Holo-3.1-9B) | |
| ## Limitations | |
| - Generated code should be reviewed before execution. | |
| - The model inherits the base model's knowledge cutoff and safety profile. | |