Instructions to use gair-prox/FW-ProX-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gair-prox/FW-ProX-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="gair-prox/FW-ProX-1.7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("gair-prox/FW-ProX-1.7B")
model = AutoModelForMultimodalLM.from_pretrained("gair-prox/FW-ProX-1.7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gair-prox/FW-ProX-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gair-prox/FW-ProX-1.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gair-prox/FW-ProX-1.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gair-prox/FW-ProX-1.7B

SGLang

How to use gair-prox/FW-ProX-1.7B 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 "gair-prox/FW-ProX-1.7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gair-prox/FW-ProX-1.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "gair-prox/FW-ProX-1.7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gair-prox/FW-ProX-1.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gair-prox/FW-ProX-1.7B with Docker Model Runner:
```
docker model run hf.co/gair-prox/FW-ProX-1.7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FW-ProX-1.7B

ArXiv | Models | Data | Code

FW-ProX-1.7B is a small language model. It was and trained on the FineWeb-pro for 50B tokens.

Evaluations

ProX models are evaluated over 10 language model benchmarks in zero-shot setting.

	ArC-c	ARC-e	CSQA	HellaS	MMLU	OBQA	PiQA	SIQA	WinoG	SciQ	AVG
raw	28.5	52.6	33.9	53.2	29.8	32.6	72.9	40.2	53.0	77.1	47.4
ours	34.4	63.9	32.6	53.0	33.1	34.4	73.1	39.3	52.7	81.5	49.8

Citation

@article{zhou2024programming,
  title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
  author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
  journal={arXiv preprint arXiv:2409.17115},
  year={2024}
}