AffectGPT Qwen2.5 7B LoRA Epoch60

--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft license: apache-2.0 datasets: - MERChallenge/MER2025 tags: - peft - lora - affectgpt - emotion-recognition - evaluation --- # AffectGPT Qwen2.5-7B LoRA (epoch 60) This repository contains only the language-model LoRA tensors extracted from the official AffectGPT epoch-60 checkpoint. It is prepared for a controlled general-capability comparison against the unchanged `Qwen/Qwen2.5-7B-Instruct` base model. ## Provenance - Upstream code: https://github.com/zeroQiaoba/AffectGPT - Upstream checkpoint: https://huggingface.co/MERChallenge/AffectGPT - Source file: `checkpoint_000060_loss_0.480.pth` - Paper: *AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models* (ICML 2025) The upstream checkpoint also contains projectors and multimodal pre-fusion weights. Those tensors are deliberately excluded here. No base-model weights are included. ## Adapter configuration - Base model: `Qwen/Qwen2.5-7B-Instruct` - PEFT type: LoRA - Rank: 16 - Alpha: 32 - Dropout used during training: 0.05 - LoRA parameter count: 40,370,176 - Target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `down_proj`, and `up_proj` in all 28 decoder layers ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_id = "Qwen/Qwen2.5-7B-Instruct" adapter_id = "TianZhenkiseki/AffectGPT-Qwen2.5-7B-LoRA-epoch60" tokenizer = AutoTokenizer.from_pretrained(base_id) base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto") model = PeftModel.from_pretrained(base, adapter_id) ``` For a fair A/B evaluation, use the same tokenizer, chat template, decoding settings, precision, and benchmark configuration for the base and adapted models. The text-only comparison measures the effect of AffectGPT's LoRA on the language backbone; it does not evaluate the complete multimodal system. ## Extraction checks The converted Safetensors file contains 392 tensors: two LoRA matrices for seven target modules across 28 layers. Layer coverage, tensor count, rank, and parameter count were validated before upload. ## Usage notice The upstream repository includes an Apache-2.0 license file and additionally describes the service as a research preview intended for non-commercial use. Users should review and follow the...

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