Update README.md

README.md

@@ -22,4 +22,144 @@ tags:
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 <h1 align="center">Athenea-4B-Thinking</h1>
 
-![image](atheneamodel.png)
+![image](atheneamodel.png)
+
+## 💻 Usage
+
+### Installation
+
+```bash
+uv pip install transformers torch accelerate
+```
+
+### Basic Inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained("Aquiles-ai/Athenea-4B-Thinking",
+        dtype=torch.bfloat16,
+        trust_remote_code=True,
+        device_map="auto",
+        attn_implementation="flash_attention_2") # Requires flash-attn
+
+# Without flash-attn:
+# model = AutoModelForCausalLM.from_pretrained("Aquiles-ai/Athenea-4B-Thinking",
+#     dtype="auto",
+#     device_map="auto"
+# )
+
+tokenizer = AutoTokenizer.from_pretrained("Aquiles-ai/Athenea-4B-Thinking", trust_remote_code=True)
+messages = [
+    {"role": "user", "content": "Hey, explain to me in simple terms how reinforcement learning works."}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to('cuda')
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=8092,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+# Decode and print the output
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+### Streaming Inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import torch
+from threading import Thread
+
+model = AutoModelForCausalLM.from_pretrained("Aquiles-ai/Athenea-4B-Thinking",
+        dtype=torch.bfloat16,
+        trust_remote_code=True,
+        device_map="auto",
+        attn_implementation="flash_attention_2")
+
+tokenizer = AutoTokenizer.from_pretrained("Aquiles-ai/Athenea-4B-Thinking", trust_remote_code=True)
+
+messages = [
+    {"role": "user", "content": "Hey, explain the difference between artificial intelligence, machine learning, and deep learning."}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to('cuda')
+
+# Create the streamer
+streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+# Build kwargs for generate
+generate_kwargs = dict(
+    **inputs,
+    max_new_tokens=8092,
+    pad_token_id=tokenizer.eos_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+    streamer=streamer,
+)
+
+def _generate_thread(model, kwargs):
+    with torch.no_grad():
+        model.generate(**kwargs)
+
+thread = Thread(target=_generate_thread, args=(model, generate_kwargs))
+
+thread.start()
+
+for chunk in streamer:
+    print(chunk, end="", flush=True)
+```
+
+### Production Deployment with vLLM
+
+**Start server:**
+
+```bash
+vllm serve Aquiles-ai/Athenea-4B-Thinking \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --api-key dummyapikey \
+  --max-model-len=16384 \
+  --async-scheduling \
+  --gpu-memory-utilization=0.90
+```
+
+**Request to the server from the OpenAI client:**
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="dummyapikey", base_url="http://127.0.0.1:8000/v1")
+
+stream = client.chat.completions.create(
+    model="Aquiles-ai/Athenea-4B-Thinking,
+    messages=[{
+        "role": "user",
+        "content": "Hey, tell me how a large language model like Llama or GPT is trained."
+    }],
+    max_tokens=8092,
+    stream=True
+)
+
+for chunk in stream:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="", flush=True)
+```
+
+**vLLM Benefits:** 20-30x faster inference, OpenAI-compatible API, continuous batching, async scheduling.