Instructions to use axonlabsai/axon-oss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use axonlabsai/axon-oss with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="axonlabsai/axon-oss")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("axonlabsai/axon-oss")
model = AutoModelForMultimodalLM.from_pretrained("axonlabsai/axon-oss")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use axonlabsai/axon-oss with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="axonlabsai/axon-oss",
	filename="axon-oss-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 axonlabsai/axon-oss with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf axonlabsai/axon-oss:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf axonlabsai/axon-oss:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf axonlabsai/axon-oss:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf axonlabsai/axon-oss: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 axonlabsai/axon-oss:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf axonlabsai/axon-oss: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 axonlabsai/axon-oss:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf axonlabsai/axon-oss:Q4_K_M

Use Docker

docker model run hf.co/axonlabsai/axon-oss:Q4_K_M

LM Studio
Jan

vLLM

How to use axonlabsai/axon-oss with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axonlabsai/axon-oss"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axonlabsai/axon-oss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/axonlabsai/axon-oss:Q4_K_M

SGLang

How to use axonlabsai/axon-oss with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "axonlabsai/axon-oss" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axonlabsai/axon-oss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "axonlabsai/axon-oss" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axonlabsai/axon-oss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use axonlabsai/axon-oss with Ollama:
```
ollama run hf.co/axonlabsai/axon-oss:Q4_K_M
```

Unsloth Studio

How to use axonlabsai/axon-oss 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 axonlabsai/axon-oss 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 axonlabsai/axon-oss to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for axonlabsai/axon-oss to start chatting

How to use axonlabsai/axon-oss with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf axonlabsai/axon-oss: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": "axonlabsai/axon-oss:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use axonlabsai/axon-oss with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf axonlabsai/axon-oss: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 axonlabsai/axon-oss:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use axonlabsai/axon-oss with Docker Model Runner:
```
docker model run hf.co/axonlabsai/axon-oss:Q4_K_M
```

Lemonade

How to use axonlabsai/axon-oss with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axonlabsai/axon-oss:Q4_K_M

Run and chat with the model

lemonade run user.axon-oss-Q4_K_M

List all available models

lemonade list

Axon OSS

A 1.7B parameter open-source chat model by Axon Labs. Built as a LoRA adapter on top of Qwen3-1.7B.

Note: This model is NOT fine-tuned for any specific task. It was created via LoRA adaptation and retains the general capabilities of the base Qwen3-1.7B model. It is not a custom-trained model from scratch.

Model Details

Base model: Qwen/Qwen3-1.7B
Parameters: ~1.7B (base) + LoRA adapter (r=16, alpha=32)
Architecture: Qwen3 (transformer decoder) with LoRA adapter targeting all linear projections (q, k, v, o, gate, up, down)
LoRA rank: 16
LoRA alpha: 32
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Tokenizer: Qwen3 tokenizer with ChatML-style formatting (<|im_start|> / <|im_end|>)
Context length: Up to 32K tokens (base model capability)
License: MIT

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "axonlabsai/axon-oss")
tokenizer = AutoTokenizer.from_pretrained("axonlabsai/axon-oss")

messages = [{"role": "user", "content": "Hello! What can you do?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

Not fine-tuned on domain-specific data — general purpose only
Small model size means limited reasoning depth compared to larger models
May hallucinate or produce incorrect information
Not suitable for production deployments without further fine-tuning

About Axon Labs

Axon Labs builds AI models and tools. This is our open-source contribution — a small, lightweight model for experimentation and chat.

Downloads last month: 20

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for axonlabsai/axon-oss

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

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Axon OPENSOURCE

Collection

Opensource axon models • 2 items • Updated 3 days ago