Update model card with metadata, links, and sample usage

Hi! I'm Niels from the Hugging Face community science team. I've updated your model card to include:
- Relevant metadata (`pipeline_tag` and `library_name`).
- A link to your GitHub repository and the paper.
- A summary of the **TerminalTraj** pipeline.
- A sample usage snippet derived from your README to make it easier for users to get started.

This will improve the discoverability and usability of the model on the Hub.

README.md

@@ -1,4 +1,56 @@
-This is the 7B model for the paper [Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments](https://huggingface.co/papers/2602.01244).
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-Coder-7B
+tags:
+- terminal-agent
+- agent
+- code
+---
+
+# TerminalTraj-7B
+
+This is the 7B model presented in the paper [Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments](https://huggingface.co/papers/2602.01244).
+
+## Introduction
+
+Training agentic models for terminal-based tasks critically depends on high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. **TerminalTraj** is a scalable pipeline that:
+1. Filters high-quality repositories to construct Dockerized execution environments.
+2. Generates Docker-aligned task instances.
+3. Synthesizes agent trajectories with executable validation code.
+
+Using TerminalTraj, the authors curated 32K Docker images and generated 50,733 verified terminal trajectories. This model is fine-tuned from the Qwen2.5-Coder-7B backbone, achieving significant performance improvements on TerminalBench.
+
+- **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)
+
+## Sample Usage
+
+You can use this model with the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "m-a-p/TerminalTraj-7B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Inference example
+prompt = "Write a bash script to find all .py files in a directory and count the lines of code."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Citation