107 lines
3.8 KiB
Markdown
107 lines
3.8 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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tags:
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- code
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- python
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- qlora
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- lora
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- peft
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- qwen2
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- text-generation
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datasets:
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- sahil2801/CodeAlpaca-20k
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# qwen2.5-coder-1.5b-CodeSLM-Nihal
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A **QLoRA fine-tune of Qwen2.5-Coder-1.5B-Instruct** into a **terse, code-first Python assistant**,
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built as a hands-on, phase-by-phase fine-tuning learning project on a single 8 GB consumer GPU
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(NVIDIA RTX 5060).
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The base model is a capable coder but chronically verbose — every answer trails a prose essay. This
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fine-tune trains it to reply with **correct, minimal code and nothing else.**
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## Headline result
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On 18 held-out prompts (greedy decoding), average output length dropped **from 272 to 60 tokens
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(−78%)** while core algorithm correctness was preserved.
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## Training curve
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| Checkpoint (epoch) | Validation loss |
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|---|---|
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| 0.13 | 0.5157 |
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| 0.38 | 0.5020 |
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| 0.88 | 0.4927 |
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| 1.00 | 0.4928 |
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Train and validation loss tracked each other the entire run — clean convergence, **no overfitting.**
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Final train loss **0.4906**; validation mean token-accuracy **~85.4%**.
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> These are training/next-token metrics plus a qualitative 18-prompt comparison. No standardized code
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> benchmark (HumanEval/MBPP) was run.
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## Training details
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| | |
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|---|---|
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| Base | `Qwen/Qwen2.5-Coder-1.5B-Instruct` |
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| Method | QLoRA (4-bit NF4 base + LoRA), completion-only loss |
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| Dataset | `sahil2801/CodeAlpaca-20k` → Qwen ChatML (19,020 train / 1,002 val) |
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| LoRA | r=16, α=32, dropout=0.05, targets q/k/v/o/gate/up/down |
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| Trainable params | 18.46M (~1.18%) |
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| Schedule | 1 epoch, 1,189 steps, LR 2e-4 cosine + 3% warmup |
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| Batch | 2 × 8 grad-accum = effective 16 |
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| Optimizer | paged_adamw_8bit, bf16, gradient checkpointing, max_len 1024 |
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| Hardware | 1× RTX 5060 (8 GB), ~50 min, peak VRAM 3.32 GB |
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## Files in this repo
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- **Root** — merged fp16 model (Transformers format); load directly with `from_pretrained`.
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- **`adapter/`** — the standalone LoRA adapter (~36 MB) to apply onto the base yourself.
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- **`gguf/`** — `…-f16.gguf` (full precision) and `…-Q4_K_M.gguf` (~986 MB) for llama.cpp / Ollama.
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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m = "MohdNihal03/qwen2.5-coder-1.5b-CodeSLM-Nihal"
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tok = AutoTokenizer.from_pretrained(m)
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model = AutoModelForCausalLM.from_pretrained(m, device_map="auto")
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msgs = [{"role": "user", "content": "Write a Python function that reverses a string without slicing."}]
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ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
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print(tok.decode(model.generate(ids, max_new_tokens=128)[0][ids.shape[1]:], skip_special_tokens=True))
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```
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### Ollama
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```
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ollama run mohdnihalll03/qwen2.5-coder-1.5b-codeslm-nihal
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```
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Prompt format: Qwen2.5 ChatML. Suggested: `temperature 0.2`, stop on `<|im_start|>` / `<|im_end|>`.
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## Limitations
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- **Style over correctness:** fine-tuning changed formatting far more than correctness. A few subtle
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base-model bugs persist (an email-regex character-class quirk; a `@timer` decorator missing
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`functools.wraps`), and one bracket-matching answer regressed to a logic bug on empty-stack input.
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**Review generated code before use.**
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- Python-focused; 1.5B params + 4-bit quantization — not a substitute for a large frontier model.
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- Occasional instruction drift (`print` vs `return`, tabulation vs memoization).
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## Attribution
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- Base: Qwen2.5-Coder-1.5B-Instruct (© Alibaba Cloud, Apache-2.0)
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- Data: `sahil2801/CodeAlpaca-20k`
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- Fine-tuned by **Nihal** as a QLoRA learning project (TRL / PEFT / bitsandbytes).
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