Instructions to use AgentPublic/fabrique-reference-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

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
```

dtrckd commited on Jan 27, 2024

Commit

2cca0aa

verified ·

1 Parent(s): bc76d3d

Upload 3 files

Browse files

add prompt template files

Files changed (3) hide show

experience_prompt_template.jinja +10 -0
expert_prompt_template.jinja +15 -0
simple_prompt_template.jinja +10 -0

experience_prompt_template.jinja ADDED Viewed

	@@ -0,0 +1,10 @@

+Mode expérience
+Question soumise au service {% if institution %}{{institution}} {% endif %}: {{query}}
+{% for chunk in experience_chunks %}
+{{chunk.id_experience}} : {{chunk.description}}
+{% endfor %}
+###Réponse :

expert_prompt_template.jinja ADDED Viewed

	@@ -0,0 +1,15 @@

+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 :

simple_prompt_template.jinja ADDED Viewed

	@@ -0,0 +1,10 @@

+Mode expérience
+Question soumise au service {% if institution %}{{institution}} {% endif %}: {{query}}
+{% if context or links %}{{"\n"}}
+Prompt:{% if context %}{{context}}{% endif %}{% if links %}{{links}}{% endif %}
+{% endif %}
+###Réponse :