Instructions to use vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ")
model = AutoModelForCausalLM.from_pretrained("vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ", device_map="auto")
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 vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ

SGLang

How to use vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ 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 "vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ" \
    --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": "vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ",
		"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 "vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ" \
        --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": "vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ with Docker Model Runner:
```
docker model run hf.co/vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Ragas Critic Model

Model to be used as part of ragas synthetic test data generation to replace GPT-4 as critic model. To know more about this visit ragas

Take it for a spin



from langchain_community.llms import VLLM

llm = VLLM(
    model="explodinggradients/Ragas-critic-llm-Qwen1.5-GPTQ",
    trust_remote_code=True,  # mandatory for hf models
    max_new_tokens=512,
    top_k=10,
    top_p=0.95,
    temperature=0.0,)


template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
input_text = """"""

simple_instruction = template.format(input_text)

llm.invoke(simple_instruction)

Generate synthetic QA pairs from Documents

Use the notebook here

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

I32

F16

vibrantlabsai
/

Ragas-critic-llm-Qwen1.5-GPTQ

You need to agree to share your contact information to access this model

Ragas Critic Model

Take it for a spin

Generate synthetic QA pairs from Documents

Space using vibrantlabsai/Ragas-critic-llm-Qwen1.5-GPTQ 1