Likich

open-coding-qwen25_7b-single_code-qlora

Deploy Dedicated

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

Output schema

Task mode: single_code.

json
{"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

python
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.

Model provider

Likich

Model tree

Base

Qwen/Qwen2.5-7B-Instruct

Adapter

this model

Modalities

Input

Text

Output

Text