Instructions to use Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw with Transformers:

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

pipe = pipeline("text-generation", model="Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw")
model = AutoModelForMultimodalLM.from_pretrained("Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw")
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 Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw

SGLang

How to use Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw 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 "Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw" \
    --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": "Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw",
		"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 "Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw" \
        --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": "Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw with Docker Model Runner:
```
docker model run hf.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw
```

Midnight-Miqu-70B-v1.5_exl2_4.0bpw / README.md

Dracones

Upload folder using huggingface_hub

dbe8ebd verified over 2 years ago

1.51 kB

base_model: []
library_name: transformers
tags:
  - mergekit
  - merge

Midnight-Miqu-70B-v1.5 - EXL2 4.0bpw

This is a 4.0bpw EXL2 quant of sophosympatheia/Midnight-Miqu-70B-v1.5

Details about the model and the merge info can be found at the above mode page.

I have not extensively tested this quant/model other than ensuring I could load it and chat with it.

Quant Details

This is the script used for quantization.

#!/bin/bash

# Activate the conda environment
source ~/miniconda3/etc/profile.d/conda.sh
conda activate exllamav2

# Define variables
MODEL_DIR="models/Midnight-Miqu-70B-v1.5"
OUTPUT_DIR="exl2_midnightv15-70b"
MEASUREMENT_FILE="measurements/midnight70b-v15.json"

BIT_PRECISIONS=(6.0 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25)

for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
do
    CONVERTED_FOLDER="models/Midnight-Miqu-70B-v1.5_exl2_${BIT_PRECISION}bpw"

    if [ -d "$CONVERTED_FOLDER" ]; then
        echo "Skipping $BIT_PRECISION as $CONVERTED_FOLDER already exists."
        continue
    fi

    rm -r "$OUTPUT_DIR"
    mkdir "$OUTPUT_DIR"
    mkdir "$CONVERTED_FOLDER"
    
    python convert.py -i "$MODEL_DIR" -o "$OUTPUT_DIR" -nr -m "$MEASUREMENT_FILE" -b "$BIT_PRECISION" -cf "$CONVERTED_FOLDER"
done