Instructions to use tensoropera/Fox-1-1.6B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensoropera/Fox-1-1.6B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="tensoropera/Fox-1-1.6B-Instruct-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tensoropera/Fox-1-1.6B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("tensoropera/Fox-1-1.6B-Instruct-v0.1")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tensoropera/Fox-1-1.6B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensoropera/Fox-1-1.6B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensoropera/Fox-1-1.6B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensoropera/Fox-1-1.6B-Instruct-v0.1

SGLang

How to use tensoropera/Fox-1-1.6B-Instruct-v0.1 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 "tensoropera/Fox-1-1.6B-Instruct-v0.1" \
    --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": "tensoropera/Fox-1-1.6B-Instruct-v0.1",
		"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 "tensoropera/Fox-1-1.6B-Instruct-v0.1" \
        --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": "tensoropera/Fox-1-1.6B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tensoropera/Fox-1-1.6B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/tensoropera/Fox-1-1.6B-Instruct-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Fox-1-1.6B-Instruct

This model is an instruction tuned model which requires alignment before it can be used in production. We will release the chat version soon.

Fox-1 is a decoder-only transformer-based small language model (SLM) with 1.6B total parameters developed by TensorOpera AI. The model was pre-trained with a 3-stage data curriculum on 3 trillion tokens of text and code data in 8K sequence length. Fox-1 uses Grouped Query Attention (GQA) with 4 key-value heads and 16 attention heads for faster inference.

Fox-1-Instruct-v0.1 is an instruction-tuned (SFT) version of Fox-1-1.6B that has an 8K native context length. The model was finetuned with 5B tokens of instruction following and multi-turn conversation data.

For the full details of this model please read Fox-1 technical report and release blog post.

Getting-Started

The model and a live inference endpoint are available on the TensorOpera AI Platform.

For detailed deployment instructions, refer to the Step-by-Step Guide on how to deploy Fox-1-Instruct on the TensorOpera AI Platform.

Benchmarks

We evaluated Fox-1 on ARC Challenge (25-shot), HellaSwag (10-shot), TruthfulQA (0-shot), MMLU (5-shot), Winogrande (5-shot), and GSM8k (5-shot). We follow the Open LLM Leaderboard's evaluation setup and report the average score of the 6 benchmarks. The model was evaluated on a machine with 8*H100 GPUs.

	Fox-1-1.6B-Instruct-v0.1	Fox-1-1.6B	Qwen1.5-1.8B-Chat	Gemma-2B-It	OpenELM-1.1B-Instruct
GSM8k	39.20%	36.39%	18.20%	4.47%	0.91%
MMLU	44.99%	43.05%	45.77%	37.70%	25.70%
ARC Challenge	43.60%	41.21%	38.99%	43.34%	40.36%
HellaSwag	63.39%	62.82%	60.31%	62.72%	71.67%
TruthfulQA	44.12%	38.66%	40.57%	45.86%	45.96%
Winogrande	62.67%	60.62%	59.51%	61.33%	61.96%
Average	49.66%	47.13%	43.89%	42.57%	41.09%