qwen2.5-coder-1.5b-CodeSLM-Nihal/README.md

license, base_model, tags, datasets, language, pipeline_tag, library_name

license	base_model	tags	datasets	language	pipeline_tag	library_name
apache-2.0	Qwen/Qwen2.5-Coder-1.5B-Instruct	code python qlora lora peft qwen2 text-generation	sahil2801/CodeAlpaca-20k	en	text-generation	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

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

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

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).