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
```

Expand same-scale benchmark comparisons

by leyili6666 - opened 18 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

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Files changed (1) hide show

README.md +3 -0

README.md CHANGED Viewed

@@ -72,6 +72,9 @@ Scores (%); evaluation settings vary by source.
 | GSM8K | Qwen2-72B-Instruct | 72B | 93.2 | **93.75** | [Qwen2.5 report, Table 6](https://arxiv.org/html/2412.15115v2#S5.SS2.SSS1) |
 | GSM8K | SciTulu-70B | 70B | 67.5 | **93.75** | [SciRIFF report, Table 7](https://arxiv.org/html/2406.07835v2#A3) |
 | GSM8K | WizardMath-Llama-RL (Llama 2) | 70B | 92.8 | **93.75** | [WizardMath report, Tables 1 & 15](https://arxiv.org/html/2308.09583v2) |
 | ARC-Easy | DeepSeek-LLM-67B-Chat | 67B | 81.6 | **84.64** | [DeepSeek official results](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) |
 | ARC-Challenge | DeepSeek-LLM-67B-Chat | 67B | 64.1 | **64.42** | [DeepSeek official results](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) |

 | GSM8K | Qwen2-72B-Instruct | 72B | 93.2 | **93.75** | [Qwen2.5 report, Table 6](https://arxiv.org/html/2412.15115v2#S5.SS2.SSS1) |
 | GSM8K | SciTulu-70B | 70B | 67.5 | **93.75** | [SciRIFF report, Table 7](https://arxiv.org/html/2406.07835v2#A3) |
 | GSM8K | WizardMath-Llama-RL (Llama 2) | 70B | 92.8 | **93.75** | [WizardMath report, Tables 1 & 15](https://arxiv.org/html/2308.09583v2) |
+| AQuA-RAT | Llama-2-70B-Chat | 70B | 31.32 | **77.56** | [Diversity of Thought paper](https://openreview.net/pdf?id=FvfhHucpLd) |
+| ARC-Easy | Llama-2-70B | 70B | 76.5 | **84.64** | [DeepSeek official results](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) |
+| ARC-Challenge | Llama-2-70B | 70B | 59.5 | **64.42** | [DeepSeek official results](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) |
 | ARC-Easy | DeepSeek-LLM-67B-Chat | 67B | 81.6 | **84.64** | [DeepSeek official results](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) |
 | ARC-Challenge | DeepSeek-LLM-67B-Chat | 67B | 64.1 | **64.42** | [DeepSeek official results](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) |