ngbcoder/flow-models-webdev-gguf

Flow Models — Web Dev (Llama 3.2 3B Instruct, Q4_K_M)

A QLoRA fine-tune of Llama-3.2-3B-Instruct on a web-development instruction dataset, exported to GGUF for use with llama.cpp, Ollama, LM Studio, or any GGUF-compatible runtime.

Model details

Base model	unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit
Fine-tuning	QLoRA (4-bit), Unsloth
Export	Merged and quantized to Q4_K_M (1.88 GB)
Context length	2048
Language	English

Training

Hyperparameter	Value
LoRA rank / alpha	16 / 16
LoRA dropout	0
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs	3
Batch size	2
Gradient accumulation	4
Learning rate	2e-4 (linear schedule)
Warmup steps	5
Weight decay	0.001
Optimizer	adamw_8bit
Max sequence length	2048
Packing	enabled
Seed	3407

Result: 87 steps, 1,376,556 tokens, training loss 1.20 → 0.39.

Dataset

formatted_webdev_dataset.json — 3,723 instruction/response examples covering HTML, CSS, JavaScript, Python, SQL, and general web development. Examples use the Alpaca prompt format and responses end with a [COMPLETE] marker.

Training text format:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
<instruction>

### Input:
<input>

### Response:
<response>
[COMPLETE]

Usage

llama.cpp

llama-cli -m Llama-3.2-3B-Instruct.Q4_K_M.gguf \
  -p "### Instruction:\nWrite a JavaScript function to count the number of vowels in a string.\n\n### Input:\n\n\n### Response:\n" \
  -n 256 --temp 0.2 -st

Ollama

ollama create flow-webdev -f Modelfile
ollama run flow-webdev

Expected output style

function countVowels(str) {
    let vowels = 'aeiou';
    let count = 0;
    for (let i = 0; i < str.length; i++) {
        if (vowels.includes(str[i].toLowerCase())) {
            count++;
        }
    }
    return count;
}
[COMPLETE]

Limitations

• Trained on a relatively small, synthetic-style web-dev dataset; it can reproduce errors or outdated patterns from that data.
• Best results come from the Alpaca prompt format it was trained on, not free-form chat.
• The [COMPLETE] suffix is part of the learned response style.
• Not evaluated for correctness, safety, or production use. Review generated code before running it.

License

Llama 3.2 Community License, inherited from the base model.