---
base_model: Hcompany/Holo-3.1-4B
library_name: peft
pipeline_tag: text-generation
tags:
- coding
- lora
- peft
- qlora
- python
---

# Holo-3.1-4B Coding LoRA

## Overview

This repository contains a PEFT LoRA adapter for `Hcompany/Holo-3.1-4B` adapted for coding-oriented instruction following and Python problem solving. The adapter is intended to be loaded on top of the base model with PEFT-compatible tooling.

## What Is Included

- LoRA adapter weights in `adapter_model.safetensors`.
- PEFT configuration in `adapter_config.json`.
- Tokenizer and chat template files copied for convenient loading.
- Evaluation and provenance artifacts from the release run.

## Training And Evaluation Summary

The adapter was produced with supervised fine-tuning on curated coding instruction data, including targeted Python problem-solving examples, broader coding instruction examples, and small external coding-instruction samples. Evaluation used an 80-task held-out greedy decoding probe drawn from HumanEval-style and MBPP-style tasks.

Measured result on the held-out probe:

- Base model: 24 / 80 tasks passed.
- Adapter model: 31 / 80 tasks passed.
- Relative lift over the measured base result: 29.17%.

These numbers are a compact functional probe, not a complete benchmark suite.

## Intended Use

Use this adapter for coding assistance experiments, Python function synthesis, small algorithmic tasks, and research on lightweight coding adaptation. Load it with PEFT on top of `Hcompany/Holo-3.1-4B`.

## Known Limitations

- The evaluation probe is small and focused on short Python tasks.
- The adapter may still fail hidden edge cases, multi-file tasks, long-context repository work, and non-Python languages.
- Outputs should be tested before use in production or security-sensitive environments.
- The adapter inherits limitations and licensing terms from the base model and training data sources.

## File List

- `adapter_model.safetensors`: LoRA adapter weights.
- `adapter_config.json`: PEFT adapter configuration.
- `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`: tokenizer/chat assets.
- `release_summary.json`: run summary and measured evaluation counts.
- `dataset_selection.json`: high-level dataset selection record.
- `eval_before_after_full_code.csv`: per-task before/after evaluation table.
- `trainer_log_history.json`: trainer log history.

## Reproducibility And Provenance

The release artifacts include dataset selection, trainer history, and before/after evaluation outputs to support auditability. The adapter was trained as a parameter-efficient LoRA continuation of the public base model and is distributed separately from the base weights.