Improve model card: Add metadata and usage example

README.md

@@ -1,7 +1,70 @@
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- agents
+- code-generation
+- tool-use
+- reinforcement-learning
+- qwen2
+---
+
 # Model Introduction
 We introduce Agent Foundation Models (AFMs), a new family built on Qwen2.5 that natively perform end-to-end, multi-turn, multi-tool problem solving—without external frameworks or manual prompting. Built on the Chain-of-Agents (CoA) paradigm, each AFM dynamically activates specialized tool and role-playing agents inside a single forward pass, emulating the cooperative reasoning of a full multi-agent system. To train these models, we distilled high-performing multi-agent trajectories into agentic supervised-fine-tuning data and further optimized performance with agentic reinforcement learning on verifiable tasks. AFMs set new state-of-the-art results on benchmarks for both web and code agents, and we release all model weights, training code, and datasets to accelerate future research on agentic AI.
 For more details, please refer to our [Projects](https://chain-of-agents-afm.github.io/), [paper](https://arxiv.org/abs/2508.13167) and [GitHub](https://github.com/OPPO-PersonalAI/Agent_Foundation_Models).
 
+## Usage
+
+You can use this model with the Hugging Face `transformers` library. Below is a simple example for inference. For more advanced usage, including training and evaluation, please refer to the [official GitHub repository](https://github.com/OPPO-PersonalAI/Agent_Foundation_Models).
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "PersonalAILab/AFM-CodeAgent-32B-rl" # This is one of the models, adjust as needed
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # or torch.float16 depending on your hardware
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Example for code agent query
+question = "Write a Python function to calculate the N-th Fibonacci number recursively."
+messages = [
+    {"role": "user", "content": question}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_k=20,
+    top_p=0.8,
+    repetition_penalty=1.1
+)
+
+generated_ids = [
+    output_ids[len(input_ids):]
+    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
 # Model Downloads
 
 | Model       |   Download   | Backbone Model                        | License|
@@ -39,4 +102,4 @@ If you find `AFM` useful in your research or applications, we would appreciate i
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2508.13167}, 
 }
-```
+```