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Model: MohdNihal03/qwen2.5-coder-1.5b-CodeSLM-Nihal
Source: Original Platform

README.md

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