neuralbroker
/

blitzkode-1.5b-lora

@@ -1,207 +1,183 @@
 ---
-base_model: Qwen/Qwen2.5-1.5B-Instruct
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
-- lora
-- transformers
 ---
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.18.1

 ---
+language:
+  - en
+license: mit
 library_name: peft
 tags:
+  - code-generation
+  - lora
+  - qwen2.5
+  - blitzkode
+  - coding-assistant
+  - fine-tuned
+  - peft
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
 ---
+# BlitzKode LoRA Adapter (0.5B)
+**BlitzKode** is a local AI coding assistant fine-tuned from
+**[Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)** using LoRA
+(Low-Rank Adaptation). This repository contains the PEFT adapter — the
+research-friendly version that can be hot-loaded on top of the base model.
+> **Creator:** [Sajad (neuralbroker)](https://github.com/neuralbroker)
+> **GitHub:** <https://github.com/neuralbroker/blitzkode>
+> **Production GGUF:** [`neuralbroker/blitzkode`](https://huggingface.co/neuralbroker/blitzkode)
+---
 ## Model Details
+| Property | Value |
+|---|---|
+| **Adapter version** | 2.1 |
+| **Base model** | `Qwen/Qwen2.5-1.5B-Instruct` |
+| **LoRA rank (r)** | 16 |
+| **LoRA alpha** | 32 |
+| **LoRA dropout** | 0.05 |
+| **Target modules** | `up_proj`, `down_proj`, `q_proj`, `o_proj`, `k_proj`, `gate_proj`, `v_proj` |
+| **Training steps** | 50 |
+| **Final loss** | ~0.48 |
+| **Library** | PEFT |
+| **License** | MIT |
+---
+## Training Pipeline
+This adapter was produced by a **4-stage fine-tuning pipeline** applied
+to the Qwen2.5 family:
+| Stage | Method | Purpose |
+|---|---|---|
+| 1 | SFT | Supervised fine-tuning on 71 curated algorithmic coding problems |
+| 2 | Reward-SFT | Continued SFT with heuristic reward signals for code correctness and formatting |
+| 3 | DPO | Direct Preference Optimization on handcrafted chosen/rejected pairs |
+| 4 | LoRA SFT (this adapter) | Final LoRA fine-tune (r=16) on 99 samples; base model Qwen2.5-0.5B |
+### Training Dataset (199 total samples)
+| Subset | Count | Source | License |
+|---|---|---|---|
+| Curated algorithmic problems | 71 | Custom (local) — arrays, strings, trees, DP, graphs | MIT |
+| MetaMathQA samples | 100 | [`meta-math/MetaMathQA`](https://huggingface.co/datasets/meta-math/MetaMathQA) | CC BY 4.0 |
+| Python/JavaScript patterns | 28 | Custom (local) — decorators, context managers, data classes | MIT |
+| **Total** | **199** | | |
+---
+## Usage
+### Load with PEFT
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base_model_id = "Qwen/Qwen2.5-1.5B-Instruct"
+adapter_repo  = "neuralbroker/blitzkode-1.5b-lora"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(model, adapter_repo)
+model.eval()
+```
+### Generate code
+```python
+prompt = (
+    "<|im_start|>system\n"
+    "You are BlitzKode, a precise AI coding assistant created by Sajad.\n"
+    "<|im_end|>\n"
+    "<|im_start|>user\n"
+    "Write a Python function for binary search with full edge-case handling.\n"
+    "<|im_end|>\n"
+    "<|im_start|>assistant\n"
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=300,
+    temperature=0.7,
+    do_sample=True,
+    repetition_penalty=1.1,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### Merge adapter into base model (for export)
+```python
+merged = model.merge_and_unload()
+merged.save_pretrained("blitzkode-0.5b-merged")
+tokenizer.save_pretrained("blitzkode-0.5b-merged")
+```
+---
+## Prompt Format
+BlitzKode uses the **ChatML** template standard for Qwen models:
+```
+<|im_start|>system
+You are BlitzKode, a precise AI coding assistant created by Sajad.<|im_end|>
+<|im_start|>user
+{your question}<|im_end|>
+<|im_start|>assistant
+```
+---
+## Limitations
+- **Text-only** — no image/multimodal support.
+- **0.5B parameters** — smaller and faster than the 1.5B GGUF variant; may be
+  less accurate on complex algorithmic tasks.
+- **2048-token context** — not suitable for long repository-level analysis.
+- **Review all outputs** — generated code must be tested before use in production.
+- **Not security-audited** — do not use for cryptographic or safety-critical code
+  without thorough expert review.
+- **Math reasoning** — MetaMathQA training improves basic reasoning but does not
+  substitute a dedicated math model.
+---
+## Relation to the Production Model
+| Variant | Repo | Size | Runtime | Use case |
+|---|---|---|---|---|
+| GGUF (1.5B, F16) | [`neuralbroker/blitzkode`](https://huggingface.co/neuralbroker/blitzkode) | ~3 GB | llama.cpp / llama-cpp-python | Production; CPU/GPU, no Python ML stack needed |
+| LoRA adapter (0.5B) | `neuralbroker/blitzkode-1.5b-lora` (this repo) | ~100 MB | PEFT + Transformers | Research; merging, further fine-tuning, quantization |
+---
+## License
+**MIT** — see [LICENSE](https://github.com/neuralbroker/blitzkode/blob/main/LICENSE).
+You must also comply with the upstream
+[Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) license
+when redistributing any derived weights.
+---
+## Citation
+```bibtex
+@software{blitzkode2025,
+  author  = {Sajad},
+  title   = {BlitzKode: A Local AI Coding Assistant},
+  year    = {2025},
+  url     = {https://github.com/neuralbroker/blitzkode}
+}
+```