Instructions to use chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B") model = PeftModel.from_pretrained(base_model, "chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora") - Notebooks
- Google Colab
- Kaggle
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Download ADAPTER_CANDIDATE_QWEN15.md from chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora: direct link, hf CLI and curl.
- Browser
- Download file 4.21 kB
-
https://huggingface.co/chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora/resolve/main/ADAPTER_CANDIDATE_QWEN15.md
- Command line
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hf download hf://chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora/ADAPTER_CANDIDATE_QWEN15.md
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curl -L -o ADAPTER_CANDIDATE_QWEN15.md https://huggingface.co/chiuratto-AIgourakis/sounio-qwen25-coder-1p5b-lora/resolve/main/ADAPTER_CANDIDATE_QWEN15.md
4.21 kB
| # Sounio LoRA Adapter Candidate — Qwen2.5-Coder-1.5B | |
| Status: candidate for Hugging Face upload, pending operator authorisation. | |
| This adapter is the first measured Sounio fine-tuning artefact in this lane that | |
| beats the zero-training base-model reference on the full 200-row held-out eval | |
| split. | |
| ## Identity | |
| | Field | Value | | |
| |---|---| | |
| | Base model | `Qwen/Qwen2.5-Coder-1.5B` | | |
| | Training objective | Sounio complete-file SFT with surgical repair pairs | | |
| | Branch | `feature/dataset-expansion` | | |
| | Dataset commit | `12c471d1a` | | |
| | Run ID | `sounio-qwen15-lora-full200-surgical-repair-20260522T021535` | | |
| | Slurm job | `1603` | | |
| | GPU | 1 GPU on `gpu-orangefs` | | |
| | Training rows requested | 4,800 | | |
| | Training rows used | 4,769 | | |
| | Optimiser steps | 120 | | |
| | Prompt style | `sounio-compact-completion` | | |
| | Repair pair ratio | 0.20 | | |
| | Run-expected ratio | 0.10 | | |
| | Separate runtime repair ratio | 0 | | |
| | Targeted runtime ratio | 0 | | |
| ## Artifact Location | |
| ```text | |
| /orangefs/training/sounio/hf-examples/lora-smoke/sounio-qwen15-lora-full200-surgical-repair-20260522T021535/results/adapter | |
| ``` | |
| Adapter files present: | |
| | File | Size | | |
| |---|---:| | |
| | `adapter_model.safetensors` | 73,911,112 bytes | | |
| | `adapter_config.json` | 797 bytes | | |
| | `README.md` | 5,097 bytes | | |
| | `tokenizer.json` | 11,421,896 bytes | | |
| | `tokenizer_config.json` | 7,338 bytes | | |
| | `vocab.json` | 2,776,833 bytes | | |
| | `merges.txt` | 1,671,853 bytes | | |
| | `special_tokens_map.json` | 616 bytes | | |
| | `added_tokens.json` | 605 bytes | | |
| Checksums: | |
| ```text | |
| 9d8540e218439bb28026115fbe8d4be5fe18162a0a1c07474d9850579ee36c4a adapter_model.safetensors | |
| 0ce16cbdfa4be1b4fb97868ee0b5f2fed33dfb3ba65c7961530f7fe7cd544057 train_summary.json | |
| 6eca4fdaaf6e3bb48f302f8090e681cddb3fc6e08b7a99dcde0f04962c09d794 lora_smoke_eval_report.json | |
| ``` | |
| ## Evaluation Result | |
| Held-out split: `datasets/hf_examples/instruction_pairs_eval.jsonl`, 200 rows. | |
| | Model/objective | Compile pass | Compile + contract-clean | Exact runtime pass | | |
| |---|---:|---:|---:| | |
| | Base model, zero training | 111/200 (55.5%) | not recorded | 25/65 (38.5%) | | |
| | Qwen2.5-Coder-1.5B LoRA candidate | 192/200 (96.0%) | 192/200 (96.0%) | 58/65 (89.2%) | | |
| | Candidate + structural repair layer | 199/200 (99.5%) | 198/200 (99.0%) | 59/65 (90.8%) | | |
| The adapter improves the two acceptance metrics used for this lane: | |
| - `souc check` pass rate on the full held-out eval split | |
| - exact stdout match on all runnable held-out rows | |
| This is a utility result, not just a training-loss result. The generated outputs | |
| extract as complete Sounio files, mostly satisfy the no-prose/no-Rust contract, | |
| compile with `souc check`, and run correctly on most rows with expected stdout. | |
| The 98%+ publication-confidence run is adapter-only: it reuses the saved adapter | |
| and widens the deterministic structural repair layer; it does not retrain model | |
| weights. Result path: | |
| ```text | |
| /orangefs/training/sounio/hf-examples/lora-smoke/sounio-qwen15-adapter-eval-98plus-20260522T083913/results | |
| ``` | |
| The structural repair layer catches outputs still outside the complete-file | |
| objective, including Rust-shaped `String`/`Vec` surfaces, invalid synthetic | |
| declarations, algebra blocks used as structs, and one observed scalar typo. | |
| ## Remaining Misses | |
| Residual failures after the 98%+ adapter-only evaluation: | |
| - compile or contract failures: 2/200 | |
| - runtime misses: 6/65 runnable rows | |
| - residual Rust-shaped surface: | |
| - `String`: 1 row | |
| - runtime families still missing: | |
| - one HumanEval runnable | |
| - two FFI logical extent rows printing `16` | |
| - two GPU tile rows printing `4` | |
| - one reserve advanced row printing `64` | |
| These are the next repair-pair targets if the goal is to improve this adapter | |
| before publication. | |
| ## Upload Policy | |
| Do not upload automatically. Upload requires explicit operator authorisation and | |
| an operator-owned Hugging Face token. | |
| Suggested repository name: | |
| ```text | |
| chiuratto-AIgourakis/sounio-qwen25-coder-15b-lora | |
| ``` | |
| Suggested upload payload: | |
| ```text | |
| adapter/ | |
| train_summary.json | |
| lora_smoke_eval_report.json | |
| lora_smoke_predictions.jsonl | |
| ADAPTER_CANDIDATE_QWEN15.md | |
| ``` | |
| The adapter should be described as an experimental Sounio LoRA adapter, not as a | |
| general-purpose production code model. | |