Instructions to use shulin16/ea-dev-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shulin16/ea-dev-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shulin16/ea-dev-final") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shulin16/ea-dev-final") model = AutoModelForCausalLM.from_pretrained("shulin16/ea-dev-final") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use shulin16/ea-dev-final with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shulin16/ea-dev-final" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shulin16/ea-dev-final", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shulin16/ea-dev-final
- SGLang
How to use shulin16/ea-dev-final with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "shulin16/ea-dev-final" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shulin16/ea-dev-final", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "shulin16/ea-dev-final" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shulin16/ea-dev-final", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use shulin16/ea-dev-final with Docker Model Runner:
docker model run hf.co/shulin16/ea-dev-final
ea-dev-final
This is checkpoint final (step 471) from fine-tuning 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
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)
# 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:
@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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