Instructions to use teolm30/fox-1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teolm30/fox-1.6 with Transformers:

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

pipe = pipeline("text-generation", model="teolm30/fox-1.6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teolm30/fox-1.6")
model = AutoModelForCausalLM.from_pretrained("teolm30/fox-1.6")
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 teolm30/fox-1.6 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="teolm30/fox-1.6",
	filename="model.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 teolm30/fox-1.6 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teolm30/fox-1.6
# Run inference directly in the terminal:
llama-cli -hf teolm30/fox-1.6

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teolm30/fox-1.6
# Run inference directly in the terminal:
llama-cli -hf teolm30/fox-1.6

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 teolm30/fox-1.6
# Run inference directly in the terminal:
./llama-cli -hf teolm30/fox-1.6

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 teolm30/fox-1.6
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teolm30/fox-1.6

Use Docker

docker model run hf.co/teolm30/fox-1.6

LM Studio
Jan

vLLM

How to use teolm30/fox-1.6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teolm30/fox-1.6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/fox-1.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teolm30/fox-1.6

SGLang

How to use teolm30/fox-1.6 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 "teolm30/fox-1.6" \
    --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": "teolm30/fox-1.6",
		"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 "teolm30/fox-1.6" \
        --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": "teolm30/fox-1.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use teolm30/fox-1.6 with Ollama:
```
ollama run hf.co/teolm30/fox-1.6
```

Unsloth Studio

How to use teolm30/fox-1.6 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 teolm30/fox-1.6 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 teolm30/fox-1.6 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for teolm30/fox-1.6 to start chatting

How to use teolm30/fox-1.6 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teolm30/fox-1.6

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": "teolm30/fox-1.6"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use teolm30/fox-1.6 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teolm30/fox-1.6

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 teolm30/fox-1.6

Run Hermes

hermes

Docker Model Runner
How to use teolm30/fox-1.6 with Docker Model Runner:
```
docker model run hf.co/teolm30/fox-1.6
```

Lemonade

How to use teolm30/fox-1.6 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teolm30/fox-1.6

Run and chat with the model

lemonade run user.fox-1.6-{{QUANT_TAG}}

List all available models

lemonade list

fox-1.6 / README.md

teolm30

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metadata

language:
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model: Qwen/Qwen3-1.7B
tags:
  - fox-1.6
  - qwen3
  - compact-llm
  - text-generation

Fox 1.6

Fox 1.6 is a compact, device-friendly fork of Qwen/Qwen3-1.7B for general-purpose text generation.

What this repo is

A lightweight Hugging Face model repo with real weights and tokenizer files
Intended to be easier to deploy on consumer hardware than much larger LLMs
Branded and published under the Fox 1.6 name

What this repo is not

It is not trained from scratch here
It is not a claim that the model beats every large frontier model
It is a fork of the upstream Qwen 3 1.7B checkpoint

Intended use

On-device or low-footprint inference
Assistant-style chat and completion
Rapid experimentation with a compact base model

Notes

If you want Fox 1.6 to become a genuinely new model, the next step is to fine-tune this fork on a curated instruction dataset and evaluate it against your target benchmarks.

🤖 Run with Ollama

ollama run hf.co/teolm30/fox-1.6