Text Generation
Transformers
Safetensors
qwen2
phai-ide
science
code
tool-use
sft
lora
conversational
text-generation-inference
Instructions to use AItonomy/PhAI-IDE-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AItonomy/PhAI-IDE-72B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AItonomy/PhAI-IDE-72B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AItonomy/PhAI-IDE-72B") model = AutoModelForCausalLM.from_pretrained("AItonomy/PhAI-IDE-72B", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AItonomy/PhAI-IDE-72B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AItonomy/PhAI-IDE-72B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AItonomy/PhAI-IDE-72B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AItonomy/PhAI-IDE-72B
- SGLang
How to use AItonomy/PhAI-IDE-72B 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 "AItonomy/PhAI-IDE-72B" \ --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": "AItonomy/PhAI-IDE-72B", "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 "AItonomy/PhAI-IDE-72B" \ --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": "AItonomy/PhAI-IDE-72B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AItonomy/PhAI-IDE-72B with Docker Model Runner:
docker model run hf.co/AItonomy/PhAI-IDE-72B
Commit History
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Make the scientific-environment table compact
Verified signatureReplace repeated full model-pair names with a short Size column (4B / 9B). Identify Qwen3.5-4B and Qwen3.5-9B and their corresponding PhAI-IDE models once in the preceding text. Keep a single table and preserve all scores.8e63418 -
Use explicit Qwen baselines in environment comparisons (#6)
- Name Qwen baselines and clarify evaluated environment scope (5d48c301b9718105b65290c6245342d03d2cf4ad) - Use explicit Qwen baselines in environment comparisons (aa3eb4aca34320e605055557dc956db7796d8dca) - Use one environment table with explicit Qwen and PhAI-IDE model pairs (c366b4cee97b6d379a1216e54d5bcd824edc623e)6308fce -
Remove additional 4B and 9B training settings from README (#5)
- Remove additional 4B and 9B training settings from README (43a3d094a9fcc4c91be22ab6698c501e462f420a)58769d2 -
Update README with verified ScienceAccelBench results (#4)
- Update README with verified ScienceAccelBench results (e646fba825b6d5aedad9ed90db2025e2e47a1134) - Unify README for the PhAI-IDE model family (53d2a7e489f3fd5cf0ae17b2d8f6c9f3602db06c) - Add benchmark gains in compact model-level tables (0d56bc13deedba1a1faf9bc422f043fba5cbc5a7) - Compact published-model comparisons; preserve science results table (7ab584926e3cfa0ca3be1211e5f411f141bd2a51) - Simplify published comparisons to complete benchmark configurations (47b47324927cc1928b60bb218846fa8d8085dde4) - Resolve README conflict with main; preserve unified model card (da68ab0daf0ee95d922654088a2720a55b6b5678)f468964 -
Expand same-scale benchmark comparisons (#2)
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