---
license: other
language:
  - en
base_model:
  - JoaoZaokk/Qwen3-4B-Thinking-2507-MiniMax-M2.1-Distill-heretic
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
tags:
  - qwen3
  - text-generation
  - code
  - python
  - lora
  - qlora
  - safetensors
  - merged
  - heretic
datasets:
  - iamtarun/python_code_instructions_18k_alpaca
  - m-a-p/CodeFeedback-Filtered-Instruction
---

# Qwen3 4B Thinking 2507 Heretic CodeFeedback

This is a merged code-focused fine-tune based on:

[`JoaoZaokk/Qwen3-4B-Thinking-2507-MiniMax-M2.1-Distill-heretic`](https://huggingface.co/JoaoZaokk/Qwen3-4B-Thinking-2507-MiniMax-M2.1-Distill-heretic)

The model was trained with QLoRA/LoRA on Python and code instruction datasets, then merged back into the base model.

This repository contains the **full merged safetensors model**, not only a LoRA adapter.

## Base model

| Item | Value |
|---|---|
| Base model | `JoaoZaokk/Qwen3-4B-Thinking-2507-MiniMax-M2.1-Distill-heretic` |
| Architecture family | Qwen3 |
| Parameter count | 4B |
| Format | Hugging Face Transformers / safetensors |
| Tensor type | F16 |
| Fine-tuning method | QLoRA / LoRA |
| Final state | Merged model |

## Training datasets

| Dataset | Samples used | Notes |
|---|---:|---|
| `iamtarun/python_code_instructions_18k_alpaca` | 5,000 | Python instruction/code examples |
| `m-a-p/CodeFeedback-Filtered-Instruction` | 5,000 | Code instruction and feedback examples |

A SWE-smith trajectory experiment was tested separately, but it was **not used** in this final merged version.

## LoRA configuration

| Parameter | Value |
|---|---:|
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Sequence length | 2048 |
| Epochs per stage | 1 |
| Quantized loading | 4-bit NF4 |
| Trainable parameters | ~33M |
| Trainable percentage | ~0.81% |

Target modules:

- `q_proj`
- `k_proj`
- `v_proj`
- `o_proj`
- `gate_proj`
- `up_proj`
- `down_proj`

## Training stages

| Stage | Input adapter | Dataset | Output adapter |
|---|---|---|---|
| 1 | Base model | Python instructions 5k | `heretic_F_lora_python_5000` |
| 2 | `heretic_F_lora_python_5000` | CodeFeedback 5k | `heretic_F_lora_python5000_codefeedback5000` |
| Final | Base model + final adapter | Merge | Full safetensors model |

## Training environment

| Component | Version |
|---|---|
| Python | 3.11 |
| PyTorch | 2.11.0+cu128 |
| CUDA | 12.8 |
| Transformers | 5.10.2 |
| Datasets | 5.0.0 |
| Accelerate | 1.13.0 |
| PEFT | 0.19.1 |
| bitsandbytes | 0.49.2 |
| sentencepiece | 0.2.1 |
| tiktoken | 0.13.0 |
| protobuf | 7.35.0 |
| pandas | 3.0.3 |
| pyarrow | 24.0.0 |

Training GPU:

- NVIDIA GeForce RTX 3080 Ti 12 GB

## Intended use

This model is intended for local experimentation with:

- Python code generation
- code explanation
- simple debugging
- instruction-following tests
- downstream conversion to GGUF, AWQ, GPTQ, or OpenVINO formats

## Notes

This is an experimental model. It may produce incorrect code, unsafe suggestions, or hallucinated explanations. Outputs should be reviewed before use in production or security-sensitive environments.

## Hardware compatibility estimate

This table is an approximate guide for the current **merged F16 safetensors** version.

| Hardware / VRAM | Status | Notes |
|---|---|---|
| 6 GB VRAM | 🔴 Unlikely | F16 weights are too large without heavy offload or quantization. |
| 8 GB VRAM | 🔴 Very tight | May fail or require CPU offload. Use GGUF/AWQ/INT4 instead. |
| 10 GB VRAM | 🟡 Possible | May run with low context and careful memory settings. |
| 12 GB VRAM | 🟢 Likely | Tested training/inference workflow on RTX 3080 Ti 12 GB with 4-bit loading. |
| 16 GB VRAM | 🟢 Good | Comfortable for normal local inference. |
| 24 GB VRAM | 🟢 Very good | Recommended for larger context, conversion, quantization, and experiments. |
| 32 GB+ RAM CPU-only | 🟡 Possible | Slow. Better with GGUF quantized versions. |

### Quantized versions

Planned/recommended export formats:

| Format | Status | Expected use |
|---|---|---|
| F16 safetensors | 🟢 Current | Full merged model, best source for conversion. |
| AWQ 4-bit | 🟡 Planned | Better for GPU/server inference, mainly CUDA/Linux or compatible runtimes. |
| OpenVINO INT4 / AWQ-style compression | 🟢 Planned for Intel Arc | Recommended path for Intel Arc/OpenVINO. |
| GGUF Q5_K_M / Q6_K / Q8_0 | 🟡 Planned | Recommended for LM Studio, llama.cpp, Ollama, CPU/GPU mixed inference. |

### Practical recommendation

For this repository, use the current F16 safetensors model as the **master model**.

For actual local use:

- RTX 3080 Ti 12 GB or better: F16 may work, but quantized versions are preferred.
- RTX 3090 24 GB: F16 and quantization workflows are much more comfortable.
- Intel Arc: convert this model to OpenVINO INT4 instead of using CUDA-focused AWQ.
- Low VRAM systems: wait for GGUF or INT4 builds.