Instructions to use Undi95/Miqu-MS-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/Miqu-MS-70B with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/Miqu-MS-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/Miqu-MS-70B")
model = AutoModelForCausalLM.from_pretrained("Undi95/Miqu-MS-70B")
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 Undi95/Miqu-MS-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/Miqu-MS-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/Miqu-MS-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Undi95/Miqu-MS-70B

SGLang

How to use Undi95/Miqu-MS-70B 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 "Undi95/Miqu-MS-70B" \
    --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": "Undi95/Miqu-MS-70B",
		"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 "Undi95/Miqu-MS-70B" \
        --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": "Undi95/Miqu-MS-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Undi95/Miqu-MS-70B with Docker Model Runner:
```
docker model run hf.co/Undi95/Miqu-MS-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Miqu-MS-70B

This is a merge of pre-trained language models created using mergekit.

The new MODEL STOCK merge method was used, see below for more information!

Feedback on this model is greatly appreciated! I hope this new merge method will be able to fill some hole Miqu have.

Others quant

Thank you all!

Prompt format

Since it was made with model using different prompt format, the following should work.

Alpaca

### Instruction:
{system prompt}

### Input:
{prompt}

### Response:
{output}

Mistral

[INST] {prompt} [/INST]

Vicuna

SYSTEM: <ANY SYSTEM CONTEXT>
USER: 
ASSISTANT:

Merge Details

Merge Method

This model was merged using the Model Stock merge method using 152334H/miqu-1-70b-sf as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: NeverSleep/MiquMaid-v2-70B
  - model: sophosympatheia/Midnight-Miqu-70B-v1.0
  - model: migtissera/Tess-70B-v1.6
  - model: 152334H/miqu-1-70b-sf
merge_method: model_stock
base_model: 152334H/miqu-1-70b-sf
dtype: bfloat16