Instructions to use anirvankrishna/model_sft_lora_fused with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anirvankrishna/model_sft_lora_fused with Transformers:

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

pipe = pipeline("text-generation", model="anirvankrishna/model_sft_lora_fused")

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

tokenizer = AutoTokenizer.from_pretrained("anirvankrishna/model_sft_lora_fused")
model = AutoModelForCausalLM.from_pretrained("anirvankrishna/model_sft_lora_fused", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anirvankrishna/model_sft_lora_fused with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anirvankrishna/model_sft_lora_fused"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anirvankrishna/model_sft_lora_fused",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anirvankrishna/model_sft_lora_fused

SGLang

How to use anirvankrishna/model_sft_lora_fused 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 "anirvankrishna/model_sft_lora_fused" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anirvankrishna/model_sft_lora_fused",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "anirvankrishna/model_sft_lora_fused" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anirvankrishna/model_sft_lora_fused",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anirvankrishna/model_sft_lora_fused with Docker Model Runner:
```
docker model run hf.co/anirvankrishna/model_sft_lora_fused
```

model_sft_lora_fused / safety_function_vector.pt

anirvankrishna

Upload safety_function_vector.pt with huggingface_hub

2b8b132 verified 7 months ago

History Blame Contribute Delete

4.82 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:: 84d4f4a6061c9321ce68a7a8696005b81d2b574fa2374b867d5f484fa3a4f699
Size of remote file:: 4.82 kB
SHA256:: 8727c1cb0c2c5114981378558e8f6d06f04697a016e1f97d040bdbb961accc4d

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

anirvankrishna
/

model_sft_lora_fused

Detected Pickle imports (3)

Xet Pointer Details