Instructions to use Arvyniitb/4bit_optimization_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arvyniitb/4bit_optimization_model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = PeftModel.from_pretrained(base_model, "Arvyniitb/4bit_optimization_model")

Transformers

How to use Arvyniitb/4bit_optimization_model with Transformers:

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

pipe = pipeline("text-generation", model="Arvyniitb/4bit_optimization_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Arvyniitb/4bit_optimization_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Arvyniitb/4bit_optimization_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Arvyniitb/4bit_optimization_model

SGLang

How to use Arvyniitb/4bit_optimization_model 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 "Arvyniitb/4bit_optimization_model" \
    --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": "Arvyniitb/4bit_optimization_model",
		"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 "Arvyniitb/4bit_optimization_model" \
        --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": "Arvyniitb/4bit_optimization_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Arvyniitb/4bit_optimization_model with Docker Model Runner:
```
docker model run hf.co/Arvyniitb/4bit_optimization_model
```

4bit_optimization_model / checkpoint-615 /scaler.pt

Arvyniitb

Upload folder using huggingface_hub

6eebe0c verified 12 months ago

History Blame Contribute Delete

1.38 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f16f55cf2cfc8497f605c0e30068013e2cf06d0764d4c3cbc11d3154080cf788
Size of remote file:: 1.38 kB
SHA256:: 59bf985af78a95922eb89c5db9a7d99258c9c2d800308c27d8ef439640af7446

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

Arvyniitb
/

4bit_optimization_model

Pickle imports

Xet Pointer Details