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

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:**