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 9 hours ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

-8

Files changed (1) hide show

README.md +6 -8

README.md CHANGED Viewed

@@ -37,16 +37,14 @@ Weight sizes are approximate; inference also requires memory for runtime allocat
 ## ScienceAccelBench performance
-**PhAI-IDE-9B** improves the task-pooled pass rate from **17.09% to 20.89% (+3.80 percentage points)** across all **27 ScienceAccelBench environments**, using **158 valid paired tasks**.
-Task-held-out, localized scientific-code repair on familiar codebases, with original numerical verification. Each row compares the initial model with its corresponding fine-tuned model on identical tasks. Pass rates are percentages; gains are percentage points.
-| Model | Environment | Tasks | Initial model | PhAI-IDE | Gain (pp) |
 | --- | --- | ---: | ---: | ---: | ---: |
-| 4B | PLUTO-Particles-Dust | 3 | 0.00 | **33.33** | **+33.33** |
-| 9B | LAPS | 16 | 31.25 | **50.00** | **+18.75** |
-| 9B | MITgcm-biogeo | 8 | 0.00 | **12.50** | **+12.50** |
-| 9B | PLUTO-RMHD | 7 | 0.00 | **28.57** | **+28.57** |
 ## Comparison with published models

 ## ScienceAccelBench performance
+Task-held-out, localized scientific-code repair on familiar codebases, with original numerical verification. Each row compares Qwen3.5 with its corresponding PhAI-IDE model on identical tasks. Pass rates are percentages; gains are percentage points.
+| Model comparison | Environment | Tasks | Qwen3.5 | PhAI-IDE | Gain (pp) |
 | --- | --- | ---: | ---: | ---: | ---: |
+| Qwen3.5-4B → PhAI-IDE-4B | PLUTO-Particles-Dust | 3 | 0.00 | **33.33** | **+33.33** |
+| Qwen3.5-9B → PhAI-IDE-9B | LAPS | 16 | 31.25 | **50.00** | **+18.75** |
+| Qwen3.5-9B → PhAI-IDE-9B | MITgcm-biogeo | 8 | 0.00 | **12.50** | **+12.50** |
+| Qwen3.5-9B → PhAI-IDE-9B | PLUTO-RMHD | 7 | 0.00 | **28.57** | **+28.57** |
 ## Comparison with published models