metadata
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- peft
- qlora
- qualitative-research
- open-coding
qwen25_7b QLoRA Open-Coding Adapter
This PEFT adapter fine-tunes Qwen/Qwen2.5-7B-Instruct to produce exactly one concise open code
for an input utterance or qualitative text segment.
Output schema
Task mode: single_code.
{"code": "short analytical label"}
Held-out verification
- Rows: 100
- Valid JSON rate: 1.000
- Non-empty rate: 1.000
- Exact set match: 0.160
- Mean set F1: 0.160
- Average generated codes: 1.000
- Verification passed: True
Exact match is reported as a format and regression diagnostic, not as a complete measure of open-code quality. Valid abstractive labels may differ in wording.
Loading
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "Likich/open-coding-qwen25_7b-single_code-qlora")
The repository contains adapter weights, tokenizer metadata, training metadata, held-out verification metrics, and sample predictions. It does not contain the full base-model weights.