How to use harsh762011/testing with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "harsh762011/testing")
How to use harsh762011/testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="harsh762011/testing") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harsh762011/testing", dtype="auto")
How to use harsh762011/testing with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "harsh762011/testing" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "harsh762011/testing", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/harsh762011/testing
How to use harsh762011/testing with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "harsh762011/testing" \ --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": "harsh762011/testing", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "harsh762011/testing" \ --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": "harsh762011/testing", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use harsh762011/testing with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for harsh762011/testing to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for harsh762011/testing to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for harsh762011/testing to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="harsh762011/testing", max_seq_length=2048, )
How to use harsh762011/testing with Docker Model Runner:
· Sign up or log in to comment
