Instructions to use GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GreenBitAI/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5", trust_remote_code=True, 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 GreenBitAI/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5

SGLang

How to use GreenBitAI/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-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/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5 with Docker Model Runner:
```
docker model run hf.co/GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5
```

GreenBit LLMs

This is GreenBitAI's pretrained low-bit LLMs with extreme compression yet still strong performance.

Please refer to our Github page for the code to run the model and more information.

zero-shot evaluation

Repository (Phi Family)	Avg Acc.	OpenBQ	ARC-E	Winogr.	HellaS.	ARC-C	PIQA	BoolQ	RACE	ANLI-R1	ANLI-R2	ANLI-R3	WiC
`Phi-3-mini-128k-instruct-layer-mix-bpw-2.2`	0.510	0.270	0.706	0.648	0.479	0.411	0.736	0.783	0.381	0.393	0.38	0.399	0.536
`Phi-3-mini-128k-instruct-layer-mix-bpw-2.5`	0.514	0.290	0.719	0.656	0.488	0.401	0.750	0.778	0.401	0.392	0.410	0.407	0.493
`Phi-3-mini-128k-instruct-layer-mix-bpw-3.0`	0.548	0.318	0.761	0.663	0.519	0.453	0.777	0.798	0.393	0.473	0.404	0.442	0.579
`Phi-3-mini-128k-instruct-layer-mix-bpw-4.0`	0.582	0.346	0.779	0.708	0.582	0.495	0.787	0.840	0.412	0.529	0.459	0.448	0.606
`Phi-3-mini-128k-instruct`	0.586	0.342	0.785	0.731	0.596	0.512	0.782	0.851	0.401	0.547	0.464	0.432	0.594

5-shot evaluation

Repository (Phi Family)	Avg Acc.	OpenBQ	ARC-E	Winogr.	HellaS.	ARC-C	PIQA	BoolQ	RACE	ANLI-R1	ANLI-R2	ANLI-R3	WiC
`Phi-3-mini-128k-instruct-layer-mix-bpw-2.2`	0.534	0.302	0.738	0.659	0.487/0.636	0.438	0.744	0.793	0.408	0.421	0.404	0.439	0.583
`Phi-3-mini-128k-instruct-layer-mix-bpw-2.5`	0.543	0.310	0.771	0.671	0.501/0.657	0.441	0.763	0.799	0.405	0.453	0.427	0.443	0.534
`Phi-3-mini-128k-instruct-layer-mix-bpw-3.0`	0.563	0.346	0.796	0.687	0.528/0.694	0.500	0.782	0.809	0.410	0.473	0.394	0.474	0.565
`Phi-3-mini-128k-instruct-layer-mix-bpw-4.0`	0.602	0.374	0.817	0.725	0.598/0.768	0.542	0.766	0.864	0.428	0.523	0.456	0.497	0.658
`Phi-3-mini-128k-instruct`	0.608	0.408	0.825	0.725	0.608/0.781	0.534	0.768	0.866		0.538	0.483	0.515	0.627

Downloads last month: 13

Safetensors

Model size

0.6B params

Tensor type

I32

F16

I16