language, license, base_model, tags, pipeline_tag, library_name

language	license	base_model	tags	pipeline_tag	library_name
en	apache-2.0	Qwen/Qwen2.5-Coder-0.5B-Instruct	code bug-fixing code-review qwen2 lora mlx ollama chatml	text-generation	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 · TIMPS · Made in India 🇮🇳

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 (`<
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.
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)

ollama pull sandeeprdy1729/timps-coder
ollama run sandeeprdy1729/timps-coder

Python (Transformers)

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)

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

License

Apache 2.0 — free to use, modify, and distribute commercially.
Base model (Qwen2.5-Coder-0.5B-Instruct) is also Apache 2.0.