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Model Details
Model Provider
bbidpa
Model Tree
Qwen/Qwen2.5-Coder-0.5B
Input Modalities
Output Modalities
Supported Functionality
🤗 Space - Diffs vs. Whole Files
Since Qwen2.5-Coder is a standard, already-registered transformers architecture, plain AutoModel loading works with no custom code:
python
from transformers import AutoModelForCausalLM, AutoTokenizer REPO = "bbidpa/Qwen2.5-Coder-0.5B-Flutter-direct" tokenizer = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained(REPO)For the exact calling convention used in the examples below (and shared with the from-scratch TinyGPT models in this collection), wrap it with these two small adapters instead:
python
import torchimport torch.nn as nnimport torch.nn.functional as Ffrom transformers import AutoTokenizer, AutoModelForCausalLM DEVICE = "cuda" if torch.cuda.is_available() else "cpu" class QwenTokenizerAdapter: def __init__(self, model_id): self.hf = AutoTokenizer.from_pretrained(model_id) self.eos_id = self.hf.eos_token_id self.bos_id = self.hf.bos_token_id if self.hf.bos_token_id is not None else self.eos_id self.pad_id = self.hf.pad_token_id if self.hf.pad_token_id is not None else self.eos_id self.vocab_size = len(self.hf) self.tokenizer = self def encode(self, text, add_special_tokens=False): return self.hf.encode(text, add_special_tokens=add_special_tokens) def decode(self, ids, skip_special_tokens=False): return self.hf.decode(ids, skip_special_tokens=skip_special_tokens) def id_to_token(self, idx): return self.hf.convert_ids_to_tokens([int(idx)])[0] def tokens(self, text): return self.hf.tokenize(text) class HFModelWrapper(nn.Module): """Matches TinyGPT's call convention -- model(xb, yb) -> (logits, loss), model.generate(idx, max_new_tokens=, eos_id=, top_k=) -> full sequence.""" def __init__(self, hf_model): super().__init__() self.hf_model = hf_model def forward(self, xb, yb=None): logits = self.hf_model(input_ids=xb).logits loss = None if yb is not None: loss = F.cross_entropy( logits.view(-1, logits.size(-1)), yb.view(-1), ignore_index=-100, ) return logits, loss def generate(self, idx, max_new_tokens, eos_id=None, top_k=None, temperature=None, do_sample=None): return self.hf_model.generate( input_ids=idx, max_new_tokens=max_new_tokens, eos_token_id=eos_id, pad_token_id=eos_id, top_k=top_k, do_sample=do_sample if do_sample is not None else (top_k is not None or temperature is not None), temperature=temperature if temperature is not None else 1.0, ) @property def vocab_size(self): return self.hf_model.config.vocab_size def load_hf_checkpoint(path, device): hf_model = AutoModelForCausalLM.from_pretrained( path, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, attn_implementation="sdpa", ).to(device) return HFModelWrapper(hf_model).to(device) tokenizer = QwenTokenizerAdapter(REPO)model = load_hf_checkpoint(REPO, DEVICE).eval()Same tag vocabulary as the rest of this collection. <CODE> holds the current file contents (empty for a from-scratch generation), <HISTORY> holds prior steps (unused for this model's typical single-step usage), and the model completes everything after <OUTPUT>:
text
<GOAL>Write a widget that displays a select button with months in it</GOAL> <CODE> </CODE> <HISTORY> </HISTORY> <OUTPUT>python
def render_step_prompt_from_data( goal: str, code: str = "", history: list[dict] | None = None, action_type: str = "", action_desc: str = "", changes: list[dict] | None = None, is_last_step: bool = False, include_output: bool = False,) -> str: history = history or [] changes = changes or [] history_text = "\n".join( f"<ACTION><TYPE>{h['type']}</TYPE><DESC>{h['desc']}</DESC></ACTION>" for h in history ) text = f"""<GOAL>{goal}</GOAL> <CODE>{code}</CODE> <HISTORY>{history_text}</HISTORY> <OUTPUT>""" if include_output: hunks = "\n".join( f"<HUNK>\n<SEARCH>\n{h['search']}\n</SEARCH>\n<REPLACE>\n{h['replace']}\n</REPLACE>\n</HUNK>" for h in changes ) output = f"<ACTION><TYPE>{action_type}</TYPE><DESC>{action_desc}</DESC></ACTION>\n<CHANGES>\n{hunks}\n</CHANGES>" if is_last_step: output += "\n<DONE></DONE>" text += output + "\n</OUTPUT>" return text def generate_output(model, tokenizer, device, prompt, max_new_tokens=300, **generate_kwargs): prompt_ids = tokenizer.encode(prompt, add_special_tokens=False) idx = torch.tensor([[tokenizer.bos_id] + prompt_ids], dtype=torch.long).to(device) generated = model.generate(idx, max_new_tokens=max_new_tokens, eos_id=tokenizer.eos_id, **generate_kwargs) text = tokenizer.decode(generated[0].tolist(), skip_special_tokens=False) output = text.split("<OUTPUT>")[-1].split("</OUTPUT>")[0].strip() return output prompt = render_step_prompt_from_data( goal="Write a widget that displays a select button with months in it",)text = generate_output(model, tokenizer, DEVICE, prompt, max_new_tokens=1024)print(text)Input goal: Write a widget that displays a select button with months in it
python
test_row = { "goal": "Write a widget that displays a select button with months in it", "code": """""", "history": [], "action_type": "", "action_desc": "", "is_last_step": False, "changes": [], } prompt = render_step_prompt_from_data( goal=test_row['goal'], code=test_row['code'], history=test_row['history'], action_type=test_row['action_type'], action_desc=test_row['action_desc'], changes=test_row['changes'], is_last_step=test_row['is_last_step'], include_output=False) text = generate_output(model, tokenizer, DEVICE, prompt, max_new_tokens=300)print(text)Output:
text
import 'package:flutter/material.dart'; class SelectMonth extends StatefulWidget { @override _SelectMonthState createState() => _SelectMonthState();} class _SelectMonthState extends State<SelectMonth> { String? selectedMonth; final List<String> monthNames = ['January', 'February', 'March', 'April', 'May', 'June']; @override Widget build(BuildContext context) { return Column( mainAxisSize: MainAxisSize.min, children: [ DropdownButton<String>( value: selectedMonth, items: monthNames .map((month) => DropdownMenuItem(value: month, child: Text(month))) .toList(), onChanged: (val) => setState(() => selectedMonth = val), ), Text('Selected month: ${selectedMonth ?? 'None'}'), ], ); }}| Base model | Qwen/Qwen2.5-Coder-0.5B (~0.5B params, pretrained by Alibaba/Qwen) |
| Fine-tuning | 5M tokens, flat goal → complete-file examples |
| Tokenizer | Qwen's native BPE tokenizer, extended with structural special tokens |
text
@misc{andrejev2026diffs, title = {Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models}, author = {Andrejev, Andrej}, year = {2026}, eprint = {2609.05779}, archivePrefix = {arXiv}, primaryClass = {cs.SE}, url = {https://arxiv.org/abs/2609.05779}}from transformers import AutoModelForCausalLM, AutoTokenizer REPO = "bbidpa/Qwen2.5-Coder-0.5B-Flutter-direct" tokenizer = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained(REPO)import torchimport torch.nn as nnimport torch.nn.functional as Ffrom transformers import AutoTokenizer, AutoModelForCausalLM DEVICE = "cuda" if torch.cuda.is_available() else "cpu" class QwenTokenizerAdapter: def __init__(self, model_id): self.hf = AutoTokenizer.from_pretrained(model_id) self.eos_id = self.hf.eos_token_id self.bos_id = self.hf.bos_token_id if self.hf.bos_token_id is not None else self.eos_id self.pad_id = self.hf.pad_token_id if self.hf.pad_token_id is not None else self.eos_id self.vocab_size = len(self.hf) self.tokenizer = self def encode(self, text, add_special_tokens=False): return self.hf.encode(text, add_special_tokens=add_special_tokens) def decode(self, ids, skip_special_tokens=False): return self.hf.decode(ids, skip_special_tokens=skip_special_tokens) def id_to_token(self, idx): return self.hf.convert_ids_to_tokens([int(idx)])[0] def tokens(self, text): return self.hf.tokenize(text) class HFModelWrapper(nn.Module): """Matches TinyGPT's call convention -- model(xb, yb) -> (logits, loss), model.generate(idx, max_new_tokens=, eos_id=, top_k=) -> full sequence.""" def __init__(self, hf_model): super().__init__() self.hf_model = hf_model def forward(self, xb, yb=None): logits = self.hf_model(input_ids=xb).logits loss = None if yb is not None: loss = F.cross_entropy( logits.view(-1, logits.size(-1)), yb.view(-1), ignore_index=-100, ) return logits, loss def generate(self, idx, max_new_tokens, eos_id=None, top_k=None, temperature=None, do_sample=None): return self.hf_model.generate( input_ids=idx, max_new_tokens=max_new_tokens, eos_token_id=eos_id, pad_token_id=eos_id, top_k=top_k, do_sample=do_sample if do_sample is not None else (top_k is not None or temperature is not None), temperature=temperature if temperature is not None else 1.0, ) @property def vocab_size(self): return self.hf_model.config.vocab_size def load_hf_checkpoint(path, device): hf_model = AutoModelForCausalLM.from_pretrained( path, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, attn_implementation="sdpa", ).to(device) return HFModelWrapper(hf_model).to(device) tokenizer = QwenTokenizerAdapter(REPO)model = load_hf_checkpoint(REPO, DEVICE).eval()<GOAL>Write a widget that displays a select button with months in it</GOAL><CODE></CODE><HISTORY></HISTORY><OUTPUT>def render_step_prompt_from_data( goal: str, code: str = "", history: list[dict] | None = None, action_type: str = "", action_desc: str = "", changes: list[dict] | None = None, is_last_step: bool = False, include_output: bool = False,) -> str: history = history or [] changes = changes or [] history_text = "\n".join( f"<ACTION><TYPE>{h['type']}</TYPE><DESC>{h['desc']}</DESC></ACTION>" for h in history ) text = f"""<GOAL>{goal}</GOAL> <CODE>{code}</CODE> <HISTORY>{history_text}</HISTORY> <OUTPUT>""" if include_output: hunks = "\n".join( f"<HUNK>\n<SEARCH>\n{h['search']}\n</SEARCH>\n<REPLACE>\n{h['replace']}\n</REPLACE>\n</HUNK>" for h in changes ) output = f"<ACTION><TYPE>{action_type}</TYPE><DESC>{action_desc}</DESC></ACTION>\n<CHANGES>\n{hunks}\n</CHANGES>" if is_last_step: output += "\n<DONE></DONE>" text += output + "\n</OUTPUT>" return text def generate_output(model, tokenizer, device, prompt, max_new_tokens=300, **generate_kwargs): prompt_ids = tokenizer.encode(prompt, add_special_tokens=False) idx = torch.tensor([[tokenizer.bos_id] + prompt_ids], dtype=torch.long).to(device) generated = model.generate(idx, max_new_tokens=max_new_tokens, eos_id=tokenizer.eos_id, **generate_kwargs) text = tokenizer.decode(generated[0].tolist(), skip_special_tokens=False) output = text.split("<OUTPUT>")[-1].split("</OUTPUT>")[0].strip() return output prompt = render_step_prompt_from_data( goal="Write a widget that displays a select button with months in it",)text = generate_output(model, tokenizer, DEVICE, prompt, max_new_tokens=1024)print(text)test_row = { "goal": "Write a widget that displays a select button with months in it", "code": """""", "history": [], "action_type": "", "action_desc": "", "is_last_step": False, "changes": [], } prompt = render_step_prompt_from_data( goal=test_row['goal'], code=test_row['code'], history=test_row['history'], action_type=test_row['action_type'], action_desc=test_row['action_desc'], changes=test_row['changes'], is_last_step=test_row['is_last_step'], include_output=False) text = generate_output(model, tokenizer, DEVICE, prompt, max_new_tokens=300)print(text)import 'package:flutter/material.dart';class SelectMonth extends StatefulWidget { @override _SelectMonthState createState() => _SelectMonthState();}class _SelectMonthState extends State<SelectMonth> { String? selectedMonth; final List<String> monthNames = ['January', 'February', 'March', 'April', 'May', 'June']; @override Widget build(BuildContext context) { return Column( mainAxisSize: MainAxisSize.min, children: [ DropdownButton<String>( value: selectedMonth, items: monthNames .map((month) => DropdownMenuItem(value: month, child: Text(month))) .toList(), onChanged: (val) => setState(() => selectedMonth = val), ), Text('Selected month: ${selectedMonth ?? 'None'}'), ], ); }}@misc{andrejev2026diffs, title = {Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models}, author = {Andrejev, Andrej}, year = {2026}, eprint = {2609.05779}, archivePrefix = {arXiv}, primaryClass = {cs.SE}, url = {https://arxiv.org/abs/2609.05779}}