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

teolm30 commited on 6 days ago

Commit

a26c9c0

verified ·

1 Parent(s): 4d8bddc

Add Ollama run instructions

Browse files

Files changed (1) hide show

README.md +45 -39

README.md CHANGED Viewed

@@ -1,39 +1,45 @@
----
-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.

+---
+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
+```bash
+ollama run hf.co/teolm30/fox-1.6
+```