Instructions to use Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw with Transformers:

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

pipe = pipeline("text-generation", model="Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw")
model = AutoModelForCausalLM.from_pretrained("Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw"
# 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-103B-v1.0_exl2_3.5bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw

SGLang

How to use Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw 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-103B-v1.0_exl2_3.5bpw" \
    --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-103B-v1.0_exl2_3.5bpw",
		"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-103B-v1.0_exl2_3.5bpw" \
        --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-103B-v1.0_exl2_3.5bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw with Docker Model Runner:
```
docker model run hf.co/Dracones/Midnight-Miqu-103B-v1.0_exl2_3.5bpw
```

cherry0328 commited on Jan 6

Commit

4519754

verified ·

1 Parent(s): f77ec56

Update README.md

Browse files

The Midnight-Miqu-103B-v1.0 model is truly impressive! We would like to contribute by updating the README to include the base_model information. This will help address the missing details in the model card.

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -1,5 +1,6 @@
 ---
-base_model: []
 library_name: transformers
 tags:
 - mergekit
@@ -43,5 +44,4 @@ mkdir $CONVERTED_FOLDER
 # Run conversion commands
 python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
 python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER
-```

 ---
+base_model:
+- sophosympatheia/Midnight-Miqu-103B-v1.0
 library_name: transformers
 tags:
 - mergekit
 # Run conversion commands
 python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
 python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER
+```