Text Generation
Transformers
Safetensors
English
phi
convAI
conversational
custom_code
Eval Results (legacy)
text-generation-inference
Instructions to use abacaj/phi-2-super with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abacaj/phi-2-super with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abacaj/phi-2-super", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abacaj/phi-2-super", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("abacaj/phi-2-super", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use abacaj/phi-2-super with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacaj/phi-2-super" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacaj/phi-2-super", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/abacaj/phi-2-super
- SGLang
How to use abacaj/phi-2-super 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 "abacaj/phi-2-super" \ --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": "abacaj/phi-2-super", "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 "abacaj/phi-2-super" \ --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": "abacaj/phi-2-super", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use abacaj/phi-2-super with Docker Model Runner:
docker model run hf.co/abacaj/phi-2-super
Dataset?
#1
by 0xbitches - opened
Hello, thanks for releasing the weights. Are there plans to open source the dataset used for cDPO as well?
also what SFT dataset was used
This comment has been hidden
SFT is 10k from: LDJnr/Capybara and 10k generated from various models.
DPO is 5k generated using the model itself.
What prompt did you use to generate the DPO dataset samples?
DPO is 5k generated using the model itself
Did you manually validate the pairs generated by the model?