Instructions to use FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal with Transformers:

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

pipe = pipeline("text-generation", model="FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal")
model = AutoModelForCausalLM.from_pretrained("FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal", 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 FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal

SGLang

How to use FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal 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 "FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal" \
    --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": "FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal",
		"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 "FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal" \
        --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": "FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal with Docker Model Runner:
```
docker model run hf.co/FluffyKaeloky/Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

Midnight-Miqu-103B-v1.5-exl2-3.5bpw-rpcal

This is a 3.5bpw EXL2 quant of FluffyKaeloky/Midnight-Miqu-103B-v1.5

The pippa file used for calibration is optimised for roleplay. The measurement file can be found in the files if you want to do your own quants.

Details about the model and the merge info can be found at the fp16 model link above.

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