Improve model card: add metadata, GitHub link, and sample usage

Hi there! I'm Niels, part of the community science team at Hugging Face.

I've opened this PR to improve your model card. The changes include:
- Adding relevant metadata tags (`pipeline_tag`, `library_name`, and `datasets`).
- Including a brief description of the model and its training background from the paper.
- Adding a sample usage section with the code snippet provided in your GitHub repository to make the model easier for the community to use.
- Linking the official GitHub repository.

Let me know if you have any questions!

README.md

@@ -1,5 +1,41 @@
+---
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- m-a-p/TerminalTraj
+---
+
+# TerminalTraj-14B
+
 This is the 14B model for the paper [Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments](https://huggingface.co/papers/2602.01244).
 
+**TerminalTraj** is a scalable pipeline designed to generate high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. It addresses the challenges of executability and verifiability by (i) filtering high-quality repositories to construct Dockerized execution environments, (ii) generating Docker-aligned task instances, and (iii) synthesizing agent trajectories with executable validation code.
+
+The model is based on the **Qwen2.5-Coder** backbone and demonstrates significant performance improvements on terminal-based agentic tasks (TerminalBench).
+
+- **GitHub Repository:** [multimodal-art-projection/TerminalTraj](https://github.com/multimodal-art-projection/TerminalTraj)
+- **Paper:** [Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments](https://huggingface.co/papers/2602.01244)
+- **Dataset:** [m-a-p/TerminalTraj](https://huggingface.co/datasets/m-a-p/TerminalTraj)
+
+## Usage
+
+You can use this model with the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "m-a-p/TerminalTraj-14B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,   # 14B建议用fp16或bf16
+    device_map="auto"            # 自动分配GPU
+)
+```
+
 ## Citation
 
 **BibTeX:**