Instructions to use sophosympatheia/Midnight-Miqu-70B-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sophosympatheia/Midnight-Miqu-70B-v1.5 with Transformers:

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

pipe = pipeline("text-generation", model="sophosympatheia/Midnight-Miqu-70B-v1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sophosympatheia/Midnight-Miqu-70B-v1.5")
model = AutoModelForCausalLM.from_pretrained("sophosympatheia/Midnight-Miqu-70B-v1.5", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sophosympatheia/Midnight-Miqu-70B-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sophosympatheia/Midnight-Miqu-70B-v1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sophosympatheia/Midnight-Miqu-70B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sophosympatheia/Midnight-Miqu-70B-v1.5

SGLang

How to use sophosympatheia/Midnight-Miqu-70B-v1.5 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 "sophosympatheia/Midnight-Miqu-70B-v1.5" \
    --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": "sophosympatheia/Midnight-Miqu-70B-v1.5",
		"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 "sophosympatheia/Midnight-Miqu-70B-v1.5" \
        --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": "sophosympatheia/Midnight-Miqu-70B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sophosympatheia/Midnight-Miqu-70B-v1.5 with Docker Model Runner:
```
docker model run hf.co/sophosympatheia/Midnight-Miqu-70B-v1.5
```

Merged a 103B version

by FluffyKaeloky - opened Mar 20, 2024

Discussion

FluffyKaeloky

Mar 20, 2024

•

edited Mar 20, 2024

Hi !
I've been using your Midnight Miqu 103B v1.0 and found it really good ! I took the liberty of creating a 103B version of your V1.5 70B model. I used the same merge parameters than your own v1.0 103B model.

https://huggingface.co/FluffyKaeloky/Midnight-Miqu-103B-v1.5

This is the first merge I do, other than quantisations, so hopefully I did not do anything wrong. I've tested it quite a lot with 32K context with no issues. I am currently uploading EXL2 quants as well.

May I ask, also, why 103B ? Since the model has 120 layers ? This gets me a bit confused.

Thanks for your amazing work ! :D

sophosympatheia

Owner Mar 20, 2024

Your merge config looks good for it. You beat me to it! I added your model to my collections for Midnight Miqu so hopefully people see it.

The number of parameters isn't quite equal to the number of layers. For example, a 70B parameter model has 80 layers and a 120B parameter model has 140 layers. 103B just happens to be the approximate number of parameters for 120 layers. I think it's a nice in between point.

FluffyKaeloky

Mar 20, 2024

Ah, I see ! Thanks for the clarification ! I'm also glad you could add it to your collection !

Fun fact, I had the files on my computer sitting for about three days, complete with exl2 quants, but my internet connection decided to die right when I was done. I'm glad to finally be able to upload all that ! I'll send you a message next time :)

sophosympatheia changed discussion status to closed Nov 21, 2024

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