eval-agent_qwen2.5-3b_instruct_ckpt100

ea-dev-checkpoint-100

This is checkpoint checkpoint-100 (step 100) from fine-tuning Qwen/Qwen2.5-3B-Instruct for evaluation agent tasks.

Checkpoint Details

• Checkpoint: checkpoint-100
• Global Step: 100
• Epoch: 0.64
• Training Loss: unknown
• Learning Rate: 9.645594202357438e-06
• 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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the checkpoint
model = AutoModelForCausalLM.from_pretrained(
    "ea-dev-checkpoint-100",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ea-dev-checkpoint-100")

# 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)

# Use with LLaMA-Factory
llamafactory-cli train \
    --stage sft \
    --model_name_or_path ea-dev-checkpoint-100 \
    --resume_from_checkpoint ea-dev-checkpoint-100

Training Progress

This checkpoint represents an intermediate state in the training process:

• Steps Completed: 100
• Epochs: 0.64
• Current Loss: unknown

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:

@misc{eval-agent-qwen2.5-checkpoint-100,
  title={Evaluation Agent Qwen2.5 Checkpoint 100},
  author={Your Name},
  year={2025},
  howpublished={\url{https://huggingface.co/ea-dev-checkpoint-100}}
}