metadata
library_name: transformers
base_model: Qwen/Qwen2.5-1.5B-Instruct
license: apache-2.0
tags:
- qwen2.5
- avito
- validation
- classification
- text-generation
- merged-lora
language:
- ru
pipeline_tag: text-generation
Avito Validation Model (Merged)
Fine-tuned Qwen2.5-1.5B-Instruct для валидации объявлений Avito. LoRA адаптер смержен с базовой моделью для удобства развертывания.
Model Details
- Base Model: Qwen/Qwen2.5-1.5B-Instruct
- Training Method: LoRA (merged)
- LoRA Rank: 16
- LoRA Alpha: 32
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Training Platform: Fireworks.ai (December 2024)
Training Stats
- Epochs: 2
- Steps: 3,333
- Training Sequences: 34,672
- Training Tokens: ~101M
- Final Loss: 0.125
Usage
Direct Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Stepan222/avito-validation-merged")
tokenizer = AutoTokenizer.from_pretrained("Stepan222/avito-validation-merged")
# Example input
messages = [
{"role": "system", "content": "Ты эксперт по валидации объявлений. Всегда отвечай строго в JSON формате."},
{"role": "user", "content": '''АРТИКУЛ: "06L121011B"
ОБЪЯВЛЕНИЯ: [{"id": "7655180983", "title": "Насос водяной VAG 06L121011B", "snippet": "...", "price": 9890.0}]'''}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))
Input Format
АРТИКУЛ: "<articulum>"
ОБЪЯВЛЕНИЯ: [
{"id": "...", "title": "...", "snippet": "...", "price": ..., "seller_reviews": ...},
...
]
Output Format
{
"passed_ids": ["id1", "id2", ...],
"rejected": [
{"id": "id3", "reason": "Причина отклонения"}
]
}
License
Apache 2.0