| --- |
| model_name: PhAI-IDE-9B |
| base_model: Qwen/Qwen3.5-9B |
| base_model_relation: finetune |
| datasets: |
| - AItonomy/ScienceIDE |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| license: apache-2.0 |
| tags: |
| - phai-ide |
| - science |
| - code |
| - tool-use |
| - sft |
| - lora |
| - safetensors |
| --- |
| |
| # PhAI-IDE-9B |
|
|
| **PhAI-IDE-9B** is a supervised fine-tune of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) for scientific coding and interaction with tools, trained using [ms-swift](https://github.com/modelscope/ms-swift). The release contains full BF16 weights with the final LoRA adapter merged. |
|
|
| The training data is sourced from [AItonomy/ScienceIDE](https://huggingface.co/datasets/AItonomy/ScienceIDE). |
|
|
| ## Quick start |
|
|
| Use Transformers 5.16.1, PyTorch and Accelerate. The example loads the model, configuration and tokenizer directly from this repository. |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForImageTextToText |
| |
| model_id = "AItonomy/PhAI-IDE-9B" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForImageTextToText.from_pretrained( |
| model_id, dtype="bfloat16", device_map="auto", |
| ) |
| inputs = tokenizer.apply_chat_template( |
| [{"role": "user", "content": "Explain how to verify a numerical simulation."}], |
| add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt", |
| ).to(model.device) |
| output = model.generate(**inputs, max_new_tokens=128, do_sample=False) |
| print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)) |
| ``` |
|
|
| BF16 weights occupy approximately 18.82 GB, plus runtime memory and KV cache. |
|
|
| ## ScienceAccelBench performance |
|
|
| Across all 27 ScienceAccelBench environments, the task-pooled pass rate increases from **17.09% to 20.89% (+3.80 percentage points)** on **158 valid paired tasks**. |
|
|
| Task-held-out, localized scientific-code repair on familiar codebases, with original numerical verification. Pass rates (%); gains in percentage points. |
|
|
| | Environment | Tasks | Qwen3.5-9B | PhAI-IDE-9B | Gain (pp) | |
| | --- | ---: | ---: | ---: | ---: | |
| | laps | 16 | 31.25 | **50.00** | **+18.75** | |
| | mitgcm-biogeo | 8 | 0.00 | **12.50** | **+12.50** | |
| | pluto-rmhd-resrmhd | 7 | 0.00 | **28.57** | **+28.57** | |
|
|
| ## Training procedure |
|
|
| ScienceIDE demonstrations were collected with **GPT-5.6-sol** and filtered using a **numerical-equivalence verifier**. They capture code inspection, tool use, and responses to execution feedback. |
|
|
| Training uses **ms-swift** supervised fine-tuning with **LoRA across trainable linear layers for three epochs**. The release merges the final checkpoint's adapter into the base model. Retained assistant targets provide the next-token training signal, while conversation history and tool observations provide context. The Codex-only data retains the native `exec` / `wait` interaction format. Heuristic target masking selects assistant actions for supervision while preserving the surrounding interaction history. |
|
|
| Fine-tuning uses text trajectories with the vision tower and aligner frozen. The merged export supports standard autoregressive Qwen3.5 generation. |
|
|
| | Setting | Value | |
| | --- | --- | |
| | Training dataset | Codex trajectories | |
| | Training examples / tasks | 4,567 segments / 564 tasks | |
| | Validation examples / tasks | 544 segments / 81 tasks | |
| | Train/validation task overlap | 0 | |
| | Training epochs | 3 | |
| | LoRA rank / alpha / dropout | 32 / 64 / 0.05 | |
| | Released weights | LoRA merged into BF16 Safetensors | |
| | Maximum training sequence length | 36,864 tokens | |
| | Learning rate / schedule | 2e-5 / cosine | |
| | Warmup ratio | 0.03 | |
| | Batch size per device / accumulation | 1 / 16 | |
| | LoRA targets | Linear modules in the unfrozen language model | |
| | Optimizer steps | 216 | |
| | Packing | Disabled | |
|
|
| Long trajectories are organized into segments. Source partition assignments are preserved, with no task identifiers shared between training and validation. |
|
|
| ### Framework versions |
|
|
| The release was validated with the following environment. |
|
|
| | Component | Version | |
| | --- | --- | |
| | Python | 3.11 | |
| | ms-swift | 4.5.3 | |
| | Transformers | 5.16.1 | |
| | PyTorch | 2.6.0+cu124 | |
| | PEFT | 0.20.0 | |
| | Datasets | 4.8.4 | |
| | Tokenizers | 0.23.2 | |
| | Accelerate | 1.14.0 | |
|
|
| ## Citations |
|
|
| The training framework is [ms-swift](https://github.com/modelscope/ms-swift), and adapter training uses [LoRA](https://arxiv.org/abs/2106.09685). |
|
|
| ```bibtex |
| @misc{zhao2024swift, |
| title = {SWIFT: A Scalable lightWeight Infrastructure for Fine-Tuning}, |
| author = {Yuze Zhao and others}, |
| year = {2024}, |
| eprint = {2408.05517}, |
| archivePrefix = {arXiv}, |
| url = {https://arxiv.org/abs/2408.05517} |
| } |
| |
| @misc{hu2021lora, |
| title = {LoRA: Low-Rank Adaptation of Large Language Models}, |
| author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen}, |
| year = {2021}, |
| eprint = {2106.09685}, |
| archivePrefix = {arXiv}, |
| url = {https://arxiv.org/abs/2106.09685} |
| } |
| ``` |
|
|
