Instructions to use IIC/RigoChat-7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IIC/RigoChat-7b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="IIC/RigoChat-7b-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IIC/RigoChat-7b-v2")
model = AutoModelForCausalLM.from_pretrained("IIC/RigoChat-7b-v2", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IIC/RigoChat-7b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IIC/RigoChat-7b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IIC/RigoChat-7b-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IIC/RigoChat-7b-v2

SGLang

How to use IIC/RigoChat-7b-v2 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 "IIC/RigoChat-7b-v2" \
    --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": "IIC/RigoChat-7b-v2",
		"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 "IIC/RigoChat-7b-v2" \
        --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": "IIC/RigoChat-7b-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IIC/RigoChat-7b-v2 with Docker Model Runner:
```
docker model run hf.co/IIC/RigoChat-7b-v2
```

gonzalo-santamaria-iic commited on Dec 5, 2024

Commit

dd12671

verified ·

1 Parent(s): 52bee2a

Update README.md

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@@ -5,7 +5,8 @@ language:
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
 pipeline_tag: text-generation
-license: cc-by-nc-4.0
 license_link: https://huggingface.co/IIC/RigoChat-7b-v2/blob/main/LICENSE
 tags:
 - chat
@@ -34,7 +35,7 @@ Remarkably, this model was trained on a single A100 GPU with limited computation
 - **Developed by:** Instituto de Ingeniería del Conocimiento (IIC).
 - **Model type:** Generative Fine-tuned Transformer.
 - **Language(s) (NLP):** Spanish (BCP-47 es).
-- **License:** CC BY NC 4.0.
 - **Finetuned from model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
 ### Model Sources

 base_model:
 - Qwen/Qwen2.5-7B-Instruct
 pipeline_tag: text-generation
+license: other
+license_name: rigochat-2-nc
 license_link: https://huggingface.co/IIC/RigoChat-7b-v2/blob/main/LICENSE
 tags:
 - chat
 - **Developed by:** Instituto de Ingeniería del Conocimiento (IIC).
 - **Model type:** Generative Fine-tuned Transformer.
 - **Language(s) (NLP):** Spanish (BCP-47 es).
+- **License:** RIGOCHAT 2 NON-COMMERCIAL.
 - **Finetuned from model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
 ### Model Sources