Instructions to use Dracones/miqu-1-70b-sf_exl2_7.0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dracones/miqu-1-70b-sf_exl2_7.0bpw with Transformers:

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

pipe = pipeline("text-generation", model="Dracones/miqu-1-70b-sf_exl2_7.0bpw")
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/miqu-1-70b-sf_exl2_7.0bpw")
model = AutoModelForCausalLM.from_pretrained("Dracones/miqu-1-70b-sf_exl2_7.0bpw", 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/miqu-1-70b-sf_exl2_7.0bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dracones/miqu-1-70b-sf_exl2_7.0bpw"
# 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/miqu-1-70b-sf_exl2_7.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dracones/miqu-1-70b-sf_exl2_7.0bpw

SGLang

How to use Dracones/miqu-1-70b-sf_exl2_7.0bpw 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/miqu-1-70b-sf_exl2_7.0bpw" \
    --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/miqu-1-70b-sf_exl2_7.0bpw",
		"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/miqu-1-70b-sf_exl2_7.0bpw" \
        --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/miqu-1-70b-sf_exl2_7.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dracones/miqu-1-70b-sf_exl2_7.0bpw with Docker Model Runner:
```
docker model run hf.co/Dracones/miqu-1-70b-sf_exl2_7.0bpw
```

Dracones commited on Apr 11, 2024

Commit

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@@ -39,49 +39,16 @@ Below are the perplexity scores for the EXL2 models. A lower score is better.
 Here are the EQ Bench scores for the EXL2 quants using Alpaca, ChatML, Mistral, Vicuna-v1.1 and Vicuna-v0 prompt templates. A higher score is better.
-| Quant Size | Instruct Template | Score |
-|------------|-------------------|-------|
-| 5.0 | ChatML | 79.91 |
-| 5.0 | Alpaca | 81.45 |
-| 5.0 | Mistral | 81.11 |
-| 5.0 | Vicuna-v1.1 | 78.37 |
-| 5.0 | Vicuna-v0 | 76.64 |
-| 4.5 | ChatML | 80.64 |
-| 4.5 | Alpaca | 80.9 |
-| 4.5 | Mistral | 81.65 |
-| 4.5 | Vicuna-v1.1 | 77.04 |
-| 4.5 | Vicuna-v0 | 74.6 |
-| 4.0 | ChatML | 80.78 |
-| 4.0 | Alpaca | 79.53 |
-| 4.0 | Mistral | 82.78 |
-| 4.0 | Vicuna-v1.1 | 79.17 |
-| 4.0 | Vicuna-v0 | 76.41 |
-| 3.5 | ChatML | 81.11 |
-| 3.5 | Alpaca | 82.42 |
-| 3.5 | Mistral | 82.34 |
-| 3.5 | Vicuna-v1.1 | 81.04 |
-| 3.5 | Vicuna-v0 | 78.09 |
-| 3.0 | ChatML | 79.13 |
-| 3.0 | Alpaca | 77.74 |
-| 3.0 | Mistral | 80.11 |
-| 3.0 | Vicuna-v1.1 | 79.38 |
-| 3.0 | Vicuna-v0 | 77.25 |
-| 2.75 | ChatML | 79.6 |
-| 2.75 | Alpaca | 77.85 |
-| 2.75 | Mistral | 79.71 |
-| 2.75 | Vicuna-v1.1 | 76.93 |
-| 2.75 | Vicuna-v0 | 75.91 |
-| 2.5 | ChatML | 77.45 |
-| 2.5 | Alpaca | 77.0 |
-| 2.5 | Mistral | 78.4 |
-| 2.5 | Vicuna-v1.1 | 75.86 |
-| 2.5 | Vicuna-v0 | 75.25 |
-| 2.25 | ChatML | 77.18 |
-| 2.25 | Alpaca | 74.06 |
-| 2.25 | Mistral | 76.75 |
-| 2.25 | Vicuna-v1.1 | 75.56 |
-| 2.25 | Vicuna-v0 | 74.28 |
 ### Perplexity Script

 Here are the EQ Bench scores for the EXL2 quants using Alpaca, ChatML, Mistral, Vicuna-v1.1 and Vicuna-v0 prompt templates. A higher score is better.
+| Quant Size | ChatML | Alpaca | Mistral | Vicuna-v1.1 | Vicuna-v0 |
+|------------|--------|--------|---------|-------------|-----------|
+| 5.0        | 79.91  | 81.45  | 81.11   | 78.37       | 76.64     |
+| 4.5        | 80.64  | 80.9   | 81.65   | 77.04       | 74.6      |
+| 4.0        | 80.78  | 79.53  | 82.78   | 79.17       | 76.41     |
+| 3.5        | 81.11  | 82.42  | 82.34   | 81.04       | 78.09     |
+| 3.0        | 79.13  | 77.74  | 80.11   | 79.38       | 77.25     |
+| 2.75       | 79.6   | 77.85  | 79.71   | 76.93       | 75.91     |
+| 2.5        | 77.45  | 77.0   | 78.4    | 75.86       | 75.25     |
+| 2.25       | 77.18  | 74.06  | 76.75   | 75.56       | 74.28     |
 ### Perplexity Script