Instructions to use AgentPublic/fabrique-reference-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AgentPublic/fabrique-reference-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AgentPublic/fabrique-reference-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AgentPublic/fabrique-reference-2") model = AutoModelForCausalLM.from_pretrained("AgentPublic/fabrique-reference-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AgentPublic/fabrique-reference-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AgentPublic/fabrique-reference-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AgentPublic/fabrique-reference-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AgentPublic/fabrique-reference-2
- SGLang
How to use AgentPublic/fabrique-reference-2 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 "AgentPublic/fabrique-reference-2" \ --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": "AgentPublic/fabrique-reference-2", "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 "AgentPublic/fabrique-reference-2" \ --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": "AgentPublic/fabrique-reference-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AgentPublic/fabrique-reference-2 with Docker Model Runner:
docker model run hf.co/AgentPublic/fabrique-reference-2
Download expert_prompt_template.jinja from AgentPublic/fabrique-reference-2: direct link, hf CLI and curl.
- Browser
- Download file 289 Bytes
-
https://huggingface.co/AgentPublic/fabrique-reference-2/resolve/main/expert_prompt_template.jinja
- Command line
-
hf download hf://AgentPublic/fabrique-reference-2/expert_prompt_template.jinja
-
curl -L -o expert_prompt_template.jinja https://huggingface.co/AgentPublic/fabrique-reference-2/resolve/main/expert_prompt_template.jinja
289 Bytes
| Mode expert | |
| Expérience: {{query}} | |
| Réponse: {{most_similar_experience}} | |
| Fiches: | |
| {% for chunk in sheet_chunks %} | |
| {{chunk.url}} : {{chunk.title}} {% if chunk.context %}({{chunk.context}}){% endif %} | |
| {{chunk.text}} {% if not loop.last %}{{"\n"}}{% endif %} | |
| {% endfor %} | |
| ###Réponse : | |