Instructions to use zjr2000/SPES-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjr2000/SPES-7B with Transformers:

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

pipe = pipeline("text-generation", model="zjr2000/SPES-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zjr2000/SPES-7B")
model = AutoModelForCausalLM.from_pretrained("zjr2000/SPES-7B")
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

vLLM

How to use zjr2000/SPES-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zjr2000/SPES-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjr2000/SPES-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zjr2000/SPES-7B

SGLang

How to use zjr2000/SPES-7B 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 "zjr2000/SPES-7B" \
    --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": "zjr2000/SPES-7B",
		"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 "zjr2000/SPES-7B" \
        --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": "zjr2000/SPES-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zjr2000/SPES-7B with Docker Model Runner:
```
docker model run hf.co/zjr2000/SPES-7B
```

Add library_name and improve model card

by nielsr HF Staff - opened 8 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+26

-17

nielsr

8 days ago

Hi! I'm Niels from the Hugging Face team. This PR improves the model card for SPES-7B by:

Adding library_name: transformers to the YAML metadata, which enables the "Use in Transformers" button and automated code snippets.
Explicitly listing the authors from the paper.
Refining the model details and project links.
Updating the BibTeX citation to include the arXiv information.

No changes were made to the model weights or configuration files.

Add library_name and improve model card073509c8

zjr2000 changed pull request status to merged 7 days ago

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