Instructions to use Equall/Saul-7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Equall/Saul-7B-Base with Transformers:

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

pipe = pipeline("text-generation", model="Equall/Saul-7B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Equall/Saul-7B-Base")
model = AutoModelForCausalLM.from_pretrained("Equall/Saul-7B-Base", 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 Equall/Saul-7B-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Equall/Saul-7B-Base

SGLang

How to use Equall/Saul-7B-Base 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 "Equall/Saul-7B-Base" \
    --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": "Equall/Saul-7B-Base",
		"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 "Equall/Saul-7B-Base" \
        --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": "Equall/Saul-7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Equall/Saul-7B-Base with Docker Model Runner:
```
docker model run hf.co/Equall/Saul-7B-Base
```

PierreColombo commited on Mar 10, 2024

Commit

2ab58d3

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Update README.md

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@@ -22,13 +22,13 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **Model type:** 7B
 - **Language(s) (NLP):** English
 - **License:** MIT
-- **Finetuned from model [optional]:** Check Saul-7B-Instruct
-### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
-- **Paper [optional]:** https://arxiv.org/abs/2403.03883
 ## Uses
@@ -65,9 +65,6 @@ This model is built upon the technology of LLM, which comes with inherent limita
 ## Citation
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
 ```bibtex

 - **Model type:** 7B
 - **Language(s) (NLP):** English
 - **License:** MIT
+- **Finetuned from model:** Check Saul-7B-Instruct
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Paper:** https://arxiv.org/abs/2403.03883
 ## Uses
 ## Citation
 ```bibtex