Text Generation
Transformers
Safetensors
English
llama
dpo
preference-optimization
PEFT
instruction-tuning
text-generation-inference
Instructions to use Likhith003/dpo-pairrm-lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Likhith003/dpo-pairrm-lora-adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Likhith003/dpo-pairrm-lora-adapter")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Likhith003/dpo-pairrm-lora-adapter") model = AutoModelForCausalLM.from_pretrained("Likhith003/dpo-pairrm-lora-adapter") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Likhith003/dpo-pairrm-lora-adapter with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Likhith003/dpo-pairrm-lora-adapter" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Likhith003/dpo-pairrm-lora-adapter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Likhith003/dpo-pairrm-lora-adapter
- SGLang
How to use Likhith003/dpo-pairrm-lora-adapter 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 "Likhith003/dpo-pairrm-lora-adapter" \ --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": "Likhith003/dpo-pairrm-lora-adapter", "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 "Likhith003/dpo-pairrm-lora-adapter" \ --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": "Likhith003/dpo-pairrm-lora-adapter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Likhith003/dpo-pairrm-lora-adapter with Docker Model Runner:
docker model run hf.co/Likhith003/dpo-pairrm-lora-adapter
DPO Fine-Tuned Adapter - PairRM Dataset
...
DPO Fine-Tuned Adapter - PairRM Dataset
π§ Model
- Base:
meta-llama/Llama-3.2-1B-Instruct - Fine-tuned using TRL's
DPOTrainerwith the PairRM preference dataset (500 pairs)
βοΈ Training Parameters
| Parameter | Value |
|---|---|
| Learning Rate | 3e-5 |
| Batch Size | 4 |
| Epochs | 3 |
| Beta (DPO regularizer) | 0.1 |
| Max Input Length | 1024 tokens |
| Max Prompt Length | 512 tokens |
| Padding Token | eos_token |
π¦ Dataset
- Source:
pairrm_preferences.csv - Size: 500 instructions with
prompt,chosen, andrejectedcolumns
π Output
- Adapter saved and uploaded as
Likhith003/dpo-pairrm-lora-adapter
- Downloads last month
- 4