Instructions to use MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp")
model = AutoModelForMultimodalLM.from_pretrained("MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp")
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 Settings

vLLM

How to use MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-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": "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp

SGLang

How to use MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-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 "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-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": "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-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 "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-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": "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp
```

Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp / README.md

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	---
	license: apache-2.0
	tags:
	- merge
	- mergekit
	- mistral
	- 7b
	- lazymergekit
	- mistralai/Mistral-7B-Instruct-v0.2
	- BAH-ML-ASC/Mistral-7B-Instruct-guidance
	---

	# Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp

	Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
	* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
	* [BAH-ML-ASC/Mistral-7B-Instruct-guidance](https://huggingface.co/BAH-ML-ASC/Mistral-7B-Instruct-guidance)

	## 🧩 Configuration

	```yaml
	slices:
	- sources:
	- model: mistralai/Mistral-7B-Instruct-v0.2
	layer_range: [0, 32]
	- model: BAH-ML-ASC/Mistral-7B-Instruct-guidance
	layer_range: [0, 32]
	merge_method: slerp
	base_model: mistralai/Mistral-7B-Instruct-v0.2
	parameters:
	t:
	- filter: self_attn
	value: [0, 0.5, 0.3, 0.7, 1]
	- filter: mlp
	value: [1, 0.5, 0.7, 0.3, 0]
	- value: 0.5
	dtype: bfloat16
	```


	## 💻 Usage


	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "MaziyarPanahi/Mistral-7B-Instruct-guidance-Mistral-7B-Instruct-v0.2-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"])
	```