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

Libraries

How to use QuantFactory/FW-ProX-1.7B-GGUF with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/FW-ProX-1.7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/FW-ProX-1.7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/FW-ProX-1.7B-GGUF: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 QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/FW-ProX-1.7B-GGUF: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 QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/FW-ProX-1.7B-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/FW-ProX-1.7B-GGUF 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 "QuantFactory/FW-ProX-1.7B-GGUF" \
    --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": "QuantFactory/FW-ProX-1.7B-GGUF",
		"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 "QuantFactory/FW-ProX-1.7B-GGUF" \
        --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": "QuantFactory/FW-ProX-1.7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/FW-ProX-1.7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/FW-ProX-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/FW-ProX-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/FW-ProX-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.FW-ProX-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/FW-ProX-1.7B-GGUF

This is quantized version of gair-prox/FW-ProX-1.7B created using llama.cpp

Original Model Card

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}
}

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GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Dataset used to train QuantFactory/FW-ProX-1.7B-GGUF

Paper for QuantFactory/FW-ProX-1.7B-GGUF

Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale

Paper • 2409.17115 • Published Sep 25, 2024 • 64