Instructions to use AItonomy/PhAI-IDE-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# 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=40)
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
```

Use explicit Qwen baselines in environment comparisons

by leyili6666 - opened about 5 hours ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

-8

leyili6666

AItonomy Foundation org about 5 hours ago

•

edited about 5 hours ago

Hugging Face CLI

Name Qwen3.5-4B / PhAI-IDE-4B and Qwen3.5-9B / PhAI-IDE-9B directly in the scientific-environment tables. Remove the overall pass-rate paragraph and environment-count or coverage claims. Preserve the individual environment scores and the remainder of the README.

Name Qwen baselines and clarify evaluated environment scope5d48c301

Use explicit Qwen baselines in environment comparisonsaa3eb4ac

leyili6666 changed pull request title from Name Qwen baselines and clarify evaluated environment scope to Use explicit Qwen baselines in environment comparisons about 5 hours ago

Use one environment table with explicit Qwen and PhAI-IDE model pairsc366b4ce

leyili6666 changed pull request status to merged about 5 hours ago

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