初始化项目，由ModelHub XC社区提供模型

Model: sandeeprdy1729/TIMPS-Coder-0.5B Source: Original Platform
2026-06-13 10:39:16 +08:00
commit 3d1e56c3f5
9 changed files with 822 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,153 @@
+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
+tags:
+- code
+- bug-fixing
+- code-review
+- qwen2
+- lora
+- mlx
+- ollama
+- chatml
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# TIMPS-Coder v3 — Elite Bug-Fixing Assistant (0.5B)
+
+> A 0.5B parameter coding model fine-tuned to **think before it codes** — specialising in bug
+> analysis, code review, algorithm problem-solving, and agentic planning.  
+> Built by [Sandeep Reddy](https://github.com/Sandeeprdy1729) · TIMPS · Made in India 🇮🇳
+
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-TIMPS--Coder--0.5B-yellow)](https://huggingface.co/sandeeprdy1729/TIMPS-Coder-0.5B)
+[![Ollama](https://img.shields.io/badge/Ollama-sandeeprdy1729%2Ftimps--coder-blue)](https://ollama.com/sandeeprdy1729/timps-coder)
+[![License](https://img.shields.io/badge/license-Apache%202.0-green)](LICENSE)
+[![Benchmark](https://img.shields.io/badge/Internal%20Benchmark-88%25%20(44%2F50)-brightgreen)](https://github.com/Sandeeprdy1729/TIMPS-Coder/blob/main/benchmark_results.json)
+
+## Model Summary
+
+| Field | Value |
+|---|---|
+| **Base model** | `Qwen/Qwen2.5-Coder-0.5B-Instruct` (Alibaba Cloud) |
+| **Architecture** | Qwen2 Transformer — 494M parameters |
+| **Fine-tuning method** | LoRA (rank=16, 16 layers) via MLX-LM |
+| **Context window** | 4096 tokens |
+| **Quantization** | Q4_K_M GGUF (Ollama) / BF16 safetensors (HuggingFace) |
+| **Chat template** | ChatML (`<|im_start|>` / `<|im_end|>`) |
+| **License** | Apache 2.0 |
+| **Training hardware** | Apple M-series (Mac M1/M2/M3, 8 GB RAM) |
+
+## Benchmark Results — 25 Tests, 5 Dimensions
+
+Evaluated on [3_benchmark_ollama.py](https://github.com/Sandeeprdy1729/TIMPS-Coder/blob/main/3_benchmark_ollama.py).  
+Scoring: **2 pts** = complete correct answer with code · **1 pt** = partial · **0** = wrong/refused.
+
+| Dimension | Score | % |
+|---|---|---|
+| 🐛 Bug Fix | 9 / 10 | **90%** |
+| 🔧 SWE / Repo-level | 9 / 10 | **90%** |
+| ⚡ Algorithms | 9 / 10 | **90%** |
+| 🔍 Code Review | 8 / 10 | **80%** |
+| 🤖 Agentic Reasoning | 9 / 10 | **90%** |
+| **TOTAL** | **44 / 50** | **88%** |
+
+## Quick Start
+
+### Ollama (recommended)
+
+```bash
+ollama pull sandeeprdy1729/timps-coder
+ollama run sandeeprdy1729/timps-coder
+```
+
+### Python (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model     = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
+tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
+
+messages = [
+    {"role": "system",  "content": "You are TIMPS-Coder v3. THINK through the root cause, FIX with complete code, VERIFY edge cases."},
+    {"role": "user",    "content": "Fix: `data['user']['email']` throws KeyError when email is absent."},
+]
+text   = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+out    = model.generate(**inputs, max_new_tokens=700, temperature=0.1, do_sample=True)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+### MLX (Mac Apple Silicon)
+
+```bash
+pip install mlx-lm
+mlx_lm.generate \
+  --model sandeeprdy1729/TIMPS-Coder-0.5B \
+  --max-tokens 700 --temp 0.1 \
+  --prompt '<|im_start|>system
+You are TIMPS-Coder v3. THINK through the root cause, FIX with complete code, VERIFY edge cases.<|im_end|>
+<|im_start|>user
+Fix the race condition: two threads increment self.count += 1 simultaneously.<|im_end|>
+<|im_start|>assistant
+'
+```
+
+## Training Details
+
+### Fine-tuning Configuration
+
+| Parameter | Value |
+|---|---|
+| Base model | `Qwen/Qwen2.5-Coder-0.5B-Instruct` |
+| Fine-tuning method | LoRA (Supervised Fine-Tuning) |
+| LoRA rank | 16 |
+| Learning rate | 5e-6 |
+| Iterations | 3,000 |
+| Batch size | 1 (grad accum ×4) |
+| Max sequence length | 2048 tokens |
+| Framework | MLX-LM on Apple Silicon |
+| Peak RAM | ~5.5 GB |
+
+### Training Data
+
+| Dataset | Type | Approx. Samples |
+|---|---|---|
+| `newfacade/LeetCodeDataset` | Algorithm problems with solutions | ~2,500 |
+| `SWE-bench/SWE-bench_Verified` | Real GitHub issue → patch | ~400 |
+| `TIGER-Lab/SWE-Next-SFT-Trajectories` | Agentic edit traces | ~2,000 |
+| `WaltonFuture/agentic-sft-new` | Tool use + bash planning | ~3,000 |
+| Custom TIMPS bug-fix corpus | Hand-curated bug/fix pairs | ~500 |
+| **Total** | | **~8,400 samples** |
+
+All samples formatted in ChatML with `THINK → FIX → VERIFY` answer structure.
+
+## Capabilities
+
+| Does well | Limitations |
+|---|---|
+| Bug root-cause analysis with explanation | Complex multi-file refactors |
+| SQL injection, race condition, memory leak detection | May miss subtle business-logic bugs |
+| O-notation analysis and algorithm optimisation | Not a replacement for static analysis tools |
+| LeetCode medium-level algorithm problems | Hard competitive programming problems |
+| GitHub Actions / CI YAML generation | Not trained on Terraform, CDK |
+
+## Usage Tips
+
+- **Temperature**: Keep at `0.1` — higher values increase hallucination on a 0.5B model
+- **Context**: Include the full function/class when asking for a bug fix
+- **Verification**: Always test generated code. Even at 88% accuracy, edge cases exist
+- **System prompt**: Required for best results — see the Quick Start examples above
+
+## Training Code
+
+Full training pipeline available at:  
+[https://github.com/Sandeeprdy1729/TIMPS-Coder](https://github.com/Sandeeprdy1729/TIMPS-Coder)
+
+## License
+
+Apache 2.0 — free to use, modify, and distribute commercially.  
+Base model (Qwen2.5-Coder-0.5B-Instruct) is also Apache 2.0.