Instructions to use GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5 with Transformers:

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

pipe = pipeline("text-generation", model="GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5")
model = AutoModelForCausalLM.from_pretrained("GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5")
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 GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5

SGLang

How to use GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5 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 "GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5" \
    --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": "GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5",
		"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 "GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5" \
        --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": "GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5 with Docker Model Runner:
```
docker model run hf.co/GreenBitAI/Qwen-1.5-4B-layer-mix-bpw-2.5
```

NicoNico commited on Apr 30, 2024

Commit

7301303

1 Parent(s): eb2c486

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@@ -5,4 +5,25 @@ license: apache-2.0
 This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
-Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.

 This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
+Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
+### Zero-shot Evaluation
+We evaluate the zero-shot ability of low-bit quantized Qwen1.5 models using the `llm_eval` library and list the results below:
+| **Repository (Qwen Family)**      | **Avg Acc.** |  **OpenBQ**  |  **ARC-E**  |  **Winogr.**  |  **HellaS.**  |  **ARC-C**  |  **PIQA**  |  **BoolQ**  |  **RACE**   |  **ANLI-R1**  |  **ANLI-R2**  |  **ANLI-R3**  |  **WiC**  |
+|:----------------------------------|:------------:|:------------:|:-----------:|:-------------:|:-------------:|:-----------:|:----------:|:-----------:|:-----------:|:-------------:|:-------------:|:-------------:|:---------:|
+| `Qwen-1.5-0.5B-layer-mix-bpw-2.2` |    0.398     |    0.170     |    0.443    |     0.527     |     0.332     |    0.238    |   0.634    |    0.620    |    0.318    |     0.332     |     0.338     |     0.330     |   0.500   |
+| `Qwen-1.5-0.5B-layer-mix-bpw-2.5` |    0.394     |    0.170     |    0.514    |     0.541     |     0.337     |    0.232    |   0.637    |    0.496    |    0.318    |     0.316     |     0.358     |     0.326     |   0.490   |
+| `Qwen-1.5-0.5B-layer-mix-bpw-3.0` |    0.407     |    0.198     |    0.533    |     0.536     |     0.348     |    0.234    |   0.671    |    0.552    |    0.323    |     0.330     |     0.333     |     0.335     |   0.495   |
+| `Qwen-1.5-1.8B-layer-mix-bpw-2.2` |    0.415     |    0.218     |    0.539    |     0.586     |     0.392     |    0.260    |   0.678    |    0.622    |    0.333    |     0.333     |     0.333     |     0.336     |   0.464   |
+| `Qwen-1.5-1.8B-layer-mix-bpw-2.5` |    0.423     |    0.222     |    0.592    |     0.585     |     0.406     |    0.267    |   0.695    |    0.629    |    0.336    |     0.314     |     0.339     |     0.361     |   0.507   |
+| `Qwen-1.5-1.8B-layer-mix-bpw-3.0` |    0.438     |    0.246     |    0.576    |     0.563     |     0.413     |    0.277    |   0.694    |    0.645    |    0.352    |     0.323     |     0.336     |     0.343     |   0.492   |
+| `Qwen-1.5-4B-layer-mix-bpw-2.2`   |    0.480     |    0.254     |    0.663    |     0.623     |     0.463     |    0.339    |   0.712    |    0.718    |    0.349    |     0.326     |     0.355     |     0.384     |   0.513   |
+| `Qwen-1.5-4B-layer-mix-bpw-2.5`   |    0.490     |    0.266     |    0.677    |     0.629     |     0.473     |    0.365    |   0.732    |    0.717    |    0.351    |     0.372     |     0.352     |     0.360     |   0.502   |
+| `Qwen-1.5-4B-layer-mix-bpw-3.0`   |    0.502     |    0.268     |    0.678    |     0.642     |     0.494     |    0.358    |   0.755    |    0.757    |    0.380    |     0.395     |     0.395     |     0.392     |   0.519   |
+| `Qwen-1.5-7B-layer-mix-bpw-2.2`   |    0.513     |    0.278     |    0.669    |     0.654     |     0.504     |    0.389    |   0.741    |    0.759    |    0.376    |     0.383     |     0.410     |     0.403     |   0.517   |
+| `Qwen-1.5-7B-layer-mix-bpw-2.5`   |    0.520     |    0.294     |    0.705    |     0.650     |     0.520     |    0.387    |   0.750    |    0.769    |    0.371    |     0.445     |     0.424     |     0.398     |   0.564   |
+| `Qwen-1.5-7B-layer-mix-bpw-3.0`   |    0.531     |    0.292     |    0.713    |     0.654     |     0.545     |    0.405    |   0.764    |    0.807    |    0.383    |     0.424     |     0.393     |     0.414     |   0.627   |
+| `Qwen-1.5-14B-layer-mix-bpw-2.5`  |    0.553     |    0.318     |    0.727    |     0.682     |     0.564     |    0.413    |   0.775    |    0.792    |    0.390    |     0.472     |     0.434     |     0.446     |   0.623   |
+| `Qwen-1.5-32B-layer-mix-bpw-3.0`  |    0.599     |    0.346     |    0.775    |     0.722     |     0.620     |    0.492    |   0.807    |    0.853    |    0.444    |     0.515     |     0.494     |     0.478     |   0.642   |