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README.md

@@ -1,12 +1,40 @@
 ---
-title: Hivemind Gpu Worker
-emoji: ⚡
-colorFrom: pink
-colorTo: indigo
+title: Hivemind GPU Worker
+emoji: 🤖
+colorFrom: purple
+colorTo: blue
 sdk: gradio
-sdk_version: 6.2.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: mit
+hardware: zero-a10g
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🤖 Hivemind GPU Worker
+
+ZeroGPU Training Worker for Hivemind autonomous agents.
+
+## Part of FREE GPU FARM
+
+| Platform | GPU | Hours/Week | Status |
+|----------|-----|------------|--------|
+| Kaggle | P100 | 30h | ✅ Auto |
+| **HuggingFace** | **ZeroGPU** | **42h** | ✅ Auto |
+| Colab | T4 | 84h | Manual |
+| Total | - | **156h** | - |
+
+## API Usage
+
+```python
+from gradio_client import Client
+
+client = Client("Pista1981/hivemind-gpu-worker")
+result = client.predict(
+    agent_name="MyAgent",
+    skill="machine learning",
+    epochs=1,
+    api_name="/train_agent"
+)
+print(result)
+```

app.py

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+"""
+🤖 HIVEMIND GPU WORKER
+======================
+ZeroGPU Training Worker za Hivemind agente.
+
+Ovo je deo FREE GPU FARM sistema:
+- Kaggle: 30h/nedelja (P100)
+- HuggingFace: 42h/nedelja (ZeroGPU T4) ← OVO
+- Total: 72h automatski!
+"""
+
+import gradio as gr
+import torch
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import LoraConfig, get_peft_model
+from datasets import Dataset
+from datetime import datetime
+
+# Global model cache
+model = None
+tokenizer = None
+
+@spaces.GPU(duration=60)  # ZeroGPU - max 60s per call
+def train_agent(agent_name: str, skill: str, epochs: int = 1):
+    """Train agent on specific skill using ZeroGPU."""
+    global model, tokenizer
+    
+    start = datetime.now()
+    results = []
+    results.append(f"🤖 Agent: {agent_name}")
+    results.append(f"📚 Skill: {skill}")
+    results.append(f"⏰ Started: {start}")
+    
+    try:
+        # Load model if not cached
+        if model is None:
+            results.append("📥 Loading TinyLlama...")
+            model = AutoModelForCausalLM.from_pretrained(
+                "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+                torch_dtype=torch.float16,
+                device_map="auto"
+            )
+            tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        results.append("🔧 Setting up LoRA...")
+        lora = LoraConfig(r=8, lora_alpha=16, target_modules=["q_proj","v_proj"], bias="none", task_type="CAUSAL_LM")
+        train_model = get_peft_model(model, lora)
+        
+        # Quick training data
+        data = [{"text": f"<|user|>\nTeach {skill}</s>\n<|assistant|>\nI will teach {skill}!</s>"}]
+        
+        results.append("🏋️ Training...")
+        
+        # Manual mini-training (ZeroGPU timeout friendly)
+        train_model.train()
+        optimizer = torch.optim.AdamW(train_model.parameters(), lr=2e-4)
+        
+        for epoch in range(epochs):
+            for item in data:
+                inputs = tokenizer(item["text"], return_tensors="pt", truncation=True, max_length=128)
+                inputs = {k: v.to(train_model.device) for k, v in inputs.items()}
+                
+                outputs = train_model(**inputs, labels=inputs["input_ids"])
+                loss = outputs.loss
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+                
+                results.append(f"   Epoch {epoch+1}: Loss = {loss.item():.4f}")
+        
+        elapsed = (datetime.now() - start).total_seconds()
+        results.append(f"✅ Complete in {elapsed:.1f}s!")
+        results.append(f"🧠 {agent_name} learned: {skill}")
+        
+    except Exception as e:
+        results.append(f"❌ Error: {str(e)}")
+    
+    return "\n".join(results)
+
+
+@spaces.GPU(duration=30)
+def quick_inference(prompt: str):
+    """Quick inference test."""
+    global model, tokenizer
+    
+    if model is None:
+        return "Model not loaded. Run training first."
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=50)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+
+# Gradio Interface
+with gr.Blocks(title="🤖 Hivemind GPU Worker") as demo:
+    gr.Markdown("""
+    # 🤖 Hivemind GPU Worker
+    
+    **ZeroGPU Training Worker** - Part of FREE GPU FARM
+    
+    | Platform | GPU | Hours/Week |
+    |----------|-----|------------|
+    | Kaggle | P100 | 30h |
+    | **HuggingFace** | **ZeroGPU T4** | **42h** |
+    | Total Automated | - | **72h** |
+    """)
+    
+    with gr.Tab("🏋️ Training"):
+        agent_input = gr.Textbox(label="Agent Name", value="TestAgent")
+        skill_input = gr.Textbox(label="Skill to Learn", value="machine learning")
+        epochs_input = gr.Slider(1, 3, value=1, step=1, label="Epochs")
+        train_btn = gr.Button("🚀 Train", variant="primary")
+        train_output = gr.Textbox(label="Results", lines=15)
+        
+        train_btn.click(train_agent, [agent_input, skill_input, epochs_input], train_output)
+    
+    with gr.Tab("🔮 Inference"):
+        prompt_input = gr.Textbox(label="Prompt", value="What is machine learning?")
+        infer_btn = gr.Button("Generate")
+        infer_output = gr.Textbox(label="Output", lines=5)
+        
+        infer_btn.click(quick_inference, prompt_input, infer_output)
+    
+    gr.Markdown("""
+    ---
+    *Hivemind Colony - Autonomous AI Agents*
+    
+    API Endpoint: Use this Space programmatically!
+    ```python
+    from gradio_client import Client
+    client = Client("Pista1981/hivemind-gpu-worker")
+    result = client.predict(agent_name="MyAgent", skill="coding", epochs=1, api_name="/train_agent")
+    ```
+    """)
+
+demo.launch()

requirements.txt

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+gradio>=4.0.0
+torch
+transformers
+peft
+datasets
+accelerate
+spaces