Kiel-2-Codex

Kiel-2-Codex is a specialized multimodal vision-language model fine-tuned on top of Qwen/Qwen3-VL-4B-Instruct. It bridges advanced visual perception with deep code generation, layout translation, and technical instruction-following.

To accommodate different hardware setups, this repository provides both quantized 4-bit weights and full 16-bit weights as separate directories, alongside the raw training adapter files.

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Repository Structure

• 4bit/ — Pre-quantized 4-bit weights (ideal for lower VRAM setups like a Tesla T4 or consumer GPUs).
• 16bit/ — Full-precision 16-bit weights (ideal for high-VRAM training/inference).
• Root files — Contains the raw LoRA adapter weights (adapter_model.safetensors), tokenizer configuration, and chat template.

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Quick Start (Python Inference)

You can easily load either version directly using Hugging Face transformers by specifying the appropriate subfolder:

Option A: Load the 4-bit Version (Recommended for standard GPUs)

import torch
from transformers import AutoModelForVision2Seq, AutoProcessor

model_id = "kiel2/kiel-2-codex"

print("Loading Kiel-2-Codex (4-bit)...")
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    subfolder="4bit",
    torch_dtype=torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

Option B: Load the 16-bit Version (Full Precision)

import torch
from transformers import AutoModelForVision2Seq, AutoProcessor

model_id = "kiel2/kiel-2-codex"

print("Loading Kiel-2-Codex (16-bit)...")
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    subfolder="16bit",
    torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

Training Details

Base Model: Qwen/Qwen3-VL-4B-Instruct

Fine-Tuning Framework: Unsloth / Hugging Face TRL & PEFT

Task Focus: Multimodal code generation and structural layout analysis.

License

This model inherits the licensing structure of its base architecture under the Apache 2.0 License.