Instructions to use josephmayo/Holo-3.1-9B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

Holo-3.1-9B-Coder

Code-specialized adaptation of 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 v2 pass@1: 37.8% on the latest rented-GPU rerun (15 October 2026). Evaluation summary. Only the aggregate score was retained from that rerun.

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 after merging the adapter into the base model. The GGUF files use K-quant mixture schemes.

Loading the adapter

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

./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

Limitations

Generated code should be reviewed before execution.
The model inherits the base model's knowledge cutoff and safety profile.

Downloads last month: 648

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for josephmayo/Holo-3.1-9B-Coder

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

Hcompany/Holo-3.1-9B

Quantized

(6)

this model

Quantizations

2 models