Instructions to use LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2

SGLang

How to use LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2 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 "LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2" \
    --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": "LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2",
		"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 "LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2" \
        --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": "LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2
```

An instruct based fine tune of mistralai/Mistral-7B-Instruct-v0.2.

It works well with long system prompts.

It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.

This model is trained on a private dataset. The high GSM8K score is NOT because of the MetaMath dataset.

Prompt Format (see the guidelines from the base model):

<s>[INST] {system_message} . Say "Acknowledged!" if you understood. [/INST] Acknowledged! </s> [INST] {prompt} [/INST]

Downloads last month: 5

Model tree for LoneStriker/Metis-0.3-6.0bpw-h6-exl2-2

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

(1096)

this model