Instructions to use FPHam/Autolycus-Mistral_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FPHam/Autolycus-Mistral_7B with Transformers:

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

pipe = pipeline("text-generation", model="FPHam/Autolycus-Mistral_7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FPHam/Autolycus-Mistral_7B")
model = AutoModelForCausalLM.from_pretrained("FPHam/Autolycus-Mistral_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 FPHam/Autolycus-Mistral_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FPHam/Autolycus-Mistral_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": "FPHam/Autolycus-Mistral_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FPHam/Autolycus-Mistral_7B

SGLang

How to use FPHam/Autolycus-Mistral_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 "FPHam/Autolycus-Mistral_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": "FPHam/Autolycus-Mistral_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 "FPHam/Autolycus-Mistral_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": "FPHam/Autolycus-Mistral_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FPHam/Autolycus-Mistral_7B with Docker Model Runner:
```
docker model run hf.co/FPHam/Autolycus-Mistral_7B
```

FPHam commited on Nov 13, 2023

Commit

308b9d0

1 Parent(s): 6ccfd2f

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -12

README.md CHANGED Viewed

@@ -39,15 +39,3 @@ The most brazen examples of 'making things up', were those rare occasions where
 "We shall use metal cages for humans!" announced Hermes triumphantly. "They will provide both protection and containment!"
-## Example
-Compare this example (Llama-precise, with Low top_p), where the Autolycus (bottom image) improves on the response by adding extra material - making it more informative, more relevant and personal ('Visit Japan') - and at the same time gives the whole thing an earthy, almost human touch.
-The OpenHermes Mistral (top image) responds, rather impersonally, in the dry tones of GPT-4.
-- Original model: [OpenHermes 2.5 Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
-<img src="https://huggingface.co/FPHam/OpenAutolycus-Mistral_7B/resolve/main/openautolycus.jpg">


39
40	"We shall use metal cages for humans!" announced Hermes triumphantly. "They will provide both protection and containment!"
41