Python code generation model — Qwen3.5-0.8B-Base fine-tuned with QLoRA (Supervised Fine-Tuning) on CodeSearchNet (Python): docstring → function code pairs.

Model Details

Property	Value
Base model	Qwen/Qwen3.5-0.8B-Base
Method	QLoRA (4-bit base + LoRA r=16, alpha=32)
Trainable params	6.4M / 759M (0.84%)
Dataset	CodeSearchNet Python — 408K samples (13,590 repos)
Task	Docstring → Python function code
Sequence length	2048
Precision	BF16
Hardware	NVIDIA RTX 4060 8GB

Training Results

Metric	Value
Train loss	0.330
Eval loss	1.214
Steps	25,524 (1 epoch)
Runtime	~25.7h

Evaluation

pass@1 (temperature 0.2), official test harness, both models in bf16.

Benchmark	Base	Fine-tuned	Improvement
HumanEval	1.2%	17.7%	14.5x
MBPP	0.0%	0.2%	0 → 1

Full report with example solutions: reports/evaluation_report.md in the training repo.

Training Details

Method: QLoRA — 4-bit quantized base + LoRA (r=16, alpha=32, dropout=0)
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer: adamw_8bit (bitsandbytes), cosine schedule, 3% warmup
Batch: 2 per device × 8 grad accumulation (effective 16)
Max sequence length: 2048
Hardware: NVIDIA RTX 4060 8GB, ~25.7h
Data: CodeSearchNet Python filtered to ≤2048 tokens (408,377 train samples)

Usage

Transformers (LoRA adapter)

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "Qwen/Qwen3.5-0.8B-Base",
    max_seq_length=2048,
    load_in_4bit=True,
)
model, tokenizer = FastLanguageModel.from_pretrained(
    "jaweed123/Qwen3.5-0.8B-Python-SFT",
    max_seq_length=2048,
    load_in_4bit=True,
)

GGUF (ollama / llama.cpp / vLLM)

bash

# llama.cpp
llama-cli -m qwen3.5-0.8b-python-sft-q4_k_m.gguf -p "Write a Python function that..."

# Ollama
ollama create qwen3.5-python -f Modelfile

dockerfile

# Modelfile
FROM qwen3.5-0.8b-python-sft-q4_k_m.gguf
TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"

Files

File	Description
`adapter_model.safetensors`	LoRA adapter (small, ~13MB)
`model.safetensors`	Merged 16-bit model
`qwen3.5-0.8b-python-sft-q4_k_m.gguf`	GGUF Q4_K_M (~0.5GB)
`qwen3.5-0.8b-python-sft-q8_0.gguf`	GGUF Q8_0 (~0.9GB)
`qwen3.5-0.8b-python-sft-f16.gguf`	GGUF F16

Limitations

Fine-tuned for Python function generation from docstrings
Trained on 2019-era open-source code
0.8B scale — limited reasoning; best for straightforward code tasks