Instructions to use labpt/ContRAG-GaMS3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use labpt/ContRAG-GaMS3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("cjvt/GaMS3-12B-Instruct")
model = PeftModel.from_pretrained(base_model, "labpt/ContRAG-GaMS3")

Transformers

How to use labpt/ContRAG-GaMS3 with Transformers:

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

pipe = pipeline("text-generation", model="labpt/ContRAG-GaMS3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("labpt/ContRAG-GaMS3", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use labpt/ContRAG-GaMS3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "labpt/ContRAG-GaMS3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "labpt/ContRAG-GaMS3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/labpt/ContRAG-GaMS3

SGLang

How to use labpt/ContRAG-GaMS3 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 "labpt/ContRAG-GaMS3" \
    --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": "labpt/ContRAG-GaMS3",
		"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 "labpt/ContRAG-GaMS3" \
        --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": "labpt/ContRAG-GaMS3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use labpt/ContRAG-GaMS3 with Docker Model Runner:
```
docker model run hf.co/labpt/ContRAG-GaMS3
```

ContRAG-GaMS3

File size: 1,109 Bytes

ebb2fb9

---
base_model: cjvt/GaMS3-12B-Instruct
library_name: peft
model_name: gams3-law-nli
tags:
- base_model:adapter:cjvt/GaMS3-12B-Instruct
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---

# Model Card for gams3-law-nli

This model is a fine-tuned version of [cjvt/GaMS3-12B-Instruct](https://huggingface.co/cjvt/GaMS3-12B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Training procedure

This model was trained with SFT.

### Framework versions

- PEFT 0.18.1
- TRL: 0.27.1
- Transformers: 4.57.0.dev0
- Pytorch: 2.8.0+cu126
- Datasets: 4.1.1
- Tokenizers: 0.22.1

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```