Instructions to use onkarsus13/Qwens_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onkarsus13/Qwens_SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="onkarsus13/Qwens_SFT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("onkarsus13/Qwens_SFT", dtype="auto") - PEFT
How to use onkarsus13/Qwens_SFT with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use onkarsus13/Qwens_SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "onkarsus13/Qwens_SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "onkarsus13/Qwens_SFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/onkarsus13/Qwens_SFT
- SGLang
How to use onkarsus13/Qwens_SFT 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 "onkarsus13/Qwens_SFT" \ --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": "onkarsus13/Qwens_SFT", "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 "onkarsus13/Qwens_SFT" \ --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": "onkarsus13/Qwens_SFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use onkarsus13/Qwens_SFT with Docker Model Runner:
docker model run hf.co/onkarsus13/Qwens_SFT
Qwens_SFT
This repository contains a model artifact fine-tuned from Qwen/Qwen3-2B-Instruct.
The model was trained to take a SMILES string and generate JSON with:
failure_summary_for_editingfailure_propertiesfailure_properties_displayediting_directions
Training data source: /home/onkarks2/transformers/fallen_angel_with_properties.csv
Example Prompt
Given the SMILES string below, generate the clinical failure editing information.
SMILES: Cc1cc(C)c(N)c(n1)C#Cc1cccc(F)c1
Return valid JSON with exactly these keys:
- "failure_summary_for_editing"
- "failure_properties"
- "failure_properties_display"
- "editing_directions"
Loading A LoRA Adapter
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = "Qwen/Qwen3-2B-Instruct"
adapter_id = "onkarsus13/Qwens_SFT"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)