Instructions to use wadhma/Refine-L2-FT-DCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wadhma/Refine-L2-FT-DCR with Transformers:

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

pipe = pipeline("text-generation", model="wadhma/Refine-L2-FT-DCR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("wadhma/Refine-L2-FT-DCR")
model = AutoModelForMultimodalLM.from_pretrained("wadhma/Refine-L2-FT-DCR")
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 Settings

vLLM

How to use wadhma/Refine-L2-FT-DCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wadhma/Refine-L2-FT-DCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wadhma/Refine-L2-FT-DCR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wadhma/Refine-L2-FT-DCR

SGLang

How to use wadhma/Refine-L2-FT-DCR 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 "wadhma/Refine-L2-FT-DCR" \
    --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": "wadhma/Refine-L2-FT-DCR",
		"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 "wadhma/Refine-L2-FT-DCR" \
        --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": "wadhma/Refine-L2-FT-DCR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wadhma/Refine-L2-FT-DCR with Docker Model Runner:
```
docker model run hf.co/wadhma/Refine-L2-FT-DCR
```

Given a document and a factually inconsistent summary and a natural language feedback, this model generates a minimally edited refinement based on the feedback.

Repository: https://github.com/ManyaWadhwa/DCR Paper: https://arxiv.org/pdf/2407.02397

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

F32

Model tree for wadhma/Refine-L2-FT-DCR

Base model

meta-llama/Llama-2-7b-chat-hf

Finetuned

(551)

this model

Dataset used to train wadhma/Refine-L2-FT-DCR

Paper for wadhma/Refine-L2-FT-DCR

Learning to Refine with Fine-Grained Natural Language Feedback

Paper • 2407.02397 • Published Jul 2, 2024