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license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- text-generation
- evaluation-agent
- cot-reasoning
- checkpoint
- qwen2.5
- video-assessment
- image-assessment
library_name: transformers
pipeline_tag: text-generation
---
# ea-dev-final
This is checkpoint **final** (step 471) from fine-tuning [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) for evaluation agent tasks.
## Checkpoint Details
- **Checkpoint**: final
- **Global Step**: 471
- **Epoch**: 3.00
- **Training Loss**: 0.8296
- **Learning Rate**: unknown
- **Base Model**: Qwen2.5-3B-Instruct
- **Task**: Multi-modal quality assessment with CoT reasoning
## Model Description
This checkpoint is from training an evaluation agent that can assess:
- **Video Quality**: Temporal consistency, motion smoothness, object consistency (VBench)
- **Image Quality**: Aesthetic quality, semantic alignment, visual fidelity (T2I-CompBench)
- **Open-ended Evaluation**: Custom quality assessment tasks
The model uses Chain-of-Thought (CoT) reasoning to provide detailed explanations for its evaluations.
## Files Included
This checkpoint contains:
- **Model Weights**: `model*.safetensors` - The actual model parameters
- **Tokenizer**: Complete tokenizer configuration and vocabulary
- **Configuration**: Model and generation configuration files
**Note**: This checkpoint contains only inference files (no optimizer states).
## Usage
### For Inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the checkpoint
model = AutoModelForCausalLM.from_pretrained(
"ea-dev-final",
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ea-dev-final")
# Example evaluation prompt
prompt = """Please evaluate the quality of this video based on the following criteria:
1. Visual quality and clarity
2. Temporal consistency
3. Motion smoothness
Video description: A person walking through a park with trees swaying in the wind.
Let me think step by step:"""
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Resume Training (if optimizer states included)
```bash
# Use with LLaMA-Factory
llamafactory-cli train \
--stage sft \
--model_name_or_path ea-dev-final \
--resume_from_checkpoint ea-dev-final
```
## Training Progress
This checkpoint represents an intermediate state in the training process:
- **Steps Completed**: 471
- **Epochs**: 3.00
- **Current Loss**: 0.8296
## Related Models
This checkpoint is part of a series. Other checkpoints from the same training run:
- Look for repositories with pattern: `ea-dev-checkpoint-*`
- Final model: `ea-dev-final`
## License
This model checkpoint is released under the Apache 2.0 license.
## Citation
If you use this checkpoint, please cite:
```bibtex
@misc{eval-agent-qwen2.5-checkpoint-471,
title={Evaluation Agent Qwen2.5 Checkpoint 471},
author={Your Name},
year={2025},
howpublished={\url{https://huggingface.co/ea-dev-final}}
}
```
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