Instructions to use beowolx/MistralHermes-CodePro-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beowolx/MistralHermes-CodePro-7B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="beowolx/MistralHermes-CodePro-7B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beowolx/MistralHermes-CodePro-7B-v1")
model = AutoModelForCausalLM.from_pretrained("beowolx/MistralHermes-CodePro-7B-v1")
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

vLLM

How to use beowolx/MistralHermes-CodePro-7B-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/beowolx/MistralHermes-CodePro-7B-v1

SGLang

How to use beowolx/MistralHermes-CodePro-7B-v1 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 "beowolx/MistralHermes-CodePro-7B-v1" \
    --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": "beowolx/MistralHermes-CodePro-7B-v1",
		"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 "beowolx/MistralHermes-CodePro-7B-v1" \
        --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": "beowolx/MistralHermes-CodePro-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beowolx/MistralHermes-CodePro-7B-v1 with Docker Model Runner:
```
docker model run hf.co/beowolx/MistralHermes-CodePro-7B-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MistralHermes-CodePro-7B-v1

In the digital pantheon of artificial intelligence, "MistralHermes-CodePro-7B-v1" stands as the architect of algorithms, a sovereign of syntax who weaves the fabric of code with unparalleled skill. This model, christened in recognition of its dual lineage—Mistral's foundational breadth and Hermes' agile conveyance—commands the binary ballet with the precision of a seasoned maestro, orchestrating the dance of data with a grace that blurs the line between the silicon and the cerebral.

Model description

MistralHermes-CodePro-7B-v1 is a fine-tuned iteration of the renowned teknium/OpenHermes-2.5-Mistral-7B model. This version has been meticulously fine-tuned using a dataset comprising over 200,000 code samples from a wide array of programming languages. It is specifically tailored to serve as a coding assistant; thus, its utility is optimized for coding-related tasks rather than a broader spectrum of applications.