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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Support me at Ko-fi

Autolycus is a son of Hermes.

Autolycus-Mistral is a refinement of OpenHermes 2.5 Mistral, designed to convert the stilted GPT-4 robotic gobbledygook into something resembling natural human English -- with just enough lies, embellishments, and downright falsehoods to bring it into line with the average newspaper article.

But what did you expect from seven billion models? You can't get good results without some level of embellishment. And besides, who cares about reality anyway? We live in a world where people believe anything they read on the Internet!

The most brazen examples of 'making things up', were those rare occasions where Autolycus actually quoted a source; usually a book title or author, sometimes a date, but which you find to be nothing more than a load of hogwash when you check it out for yourself.

"I have no idea why anyone would want to build such a thing, other than being bored or having too much time on their hands," said Hermes dismissively.

"It has been done before," said another voice, this time belonging to Hermes' son, Autolycus. "Back in ancient Greece, there was a man called Daedalus who built himself wings made of feathers and wax so he could fly away from King Minos of Crete."

"Yes, but we are not talking about birds here!" exclaimed Hermes impatiently. "We need to figure out how to keep humans from running off all over the place once they become airborne." He paused thoughtfully then continued, "There must be some way..." His eyes lit up suddenly, and he clapped his hands together excitedly. "Of course! Why didn't I see this sooner?"

"What?" asked Autolycus curiously.

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

Model uses ChatML

<|im_start|>system
<|im_end|>
<|im_start|>user
How small are the atoms?<|im_end|>
<|im_start|>assistant

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for FPHam/Autolycus-Mistral_7B

Quantizations

6 models