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

Libraries

How to use paulilioaica/Hugo-7B-slerp with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("paulilioaica/Hugo-7B-slerp")
model = AutoModelForCausalLM.from_pretrained("paulilioaica/Hugo-7B-slerp", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use paulilioaica/Hugo-7B-slerp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/paulilioaica/Hugo-7B-slerp

SGLang

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

Docker Model Runner
How to use paulilioaica/Hugo-7B-slerp with Docker Model Runner:
```
docker model run hf.co/paulilioaica/Hugo-7B-slerp
```

paulilioaica commited on Jan 28, 2024

Commit

ec9fa5a

verified ·

1 Parent(s): 820dcd2

Update README.md

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Files changed (1) hide show

README.md +3 -4

README.md CHANGED Viewed

@@ -51,14 +51,13 @@ model = "paulilioaica/Hugo-7B-slerp"
 messages = [{"role": "user", "content": "What is a large language model?"}]
 tokenizer = AutoTokenizer.from_pretrained(model)
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 pipeline = transformers.pipeline(
-    "text-generation",
     model=model,
     torch_dtype=torch.float16,
     device_map="auto",
 )
-outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
-print(outputs[0]["generated_text"])
 ```

 messages = [{"role": "user", "content": "What is a large language model?"}]
 tokenizer = AutoTokenizer.from_pretrained(model)
 pipeline = transformers.pipeline(
+    "conversation",
     model=model,
     torch_dtype=torch.float16,
     device_map="auto",
 )
+outputs = pipeline(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs)
 ```