Upload upload_to_hf.py

upload_to_hf.py

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+# upload_to_hf.py - Script to upload your Mamba Swarm to HuggingFace
+
+import os
+import shutil
+from huggingface_hub import HfApi, upload_folder
+import json
+
+def prepare_model_repo():
+    """Prepare model repository structure for HuggingFace"""
+    
+    # Create required files for HuggingFace model
+    model_files = {
+        "README.md": create_model_readme(),
+        "config.json": create_model_config(),
+        "requirements.txt": create_requirements(),
+        "modeling_mamba_swarm.py": create_modeling_file()
+    }
+    
+    # Create model repo directory
+    os.makedirs("hf_model_repo", exist_ok=True)
+    
+    # Copy your mamba_swarm code
+    shutil.copytree("mamba_swarm", "hf_model_repo/mamba_swarm", dirs_exist_ok=True)
+    
+    # Create HuggingFace specific files
+    for filename, content in model_files.items():
+        with open(f"hf_model_repo/{filename}", "w") as f:
+            f.write(content)
+    
+    print("Model repository prepared!")
+
+def create_model_readme():
+    return """---
+license: apache-2.0
+language: 
+- en
+pipeline_tag: text-generation
+tags:
+- mamba
+- swarm
+- routing
+- language-model
+---
+
+# Mamba Swarm: Dynamic Routing Language Model
+
+A novel architecture combining 100 specialized Mamba encoders with dynamic routing and aggregation for efficient language modeling.
+
+## Architecture
+
+- **100 Mamba Encoders**: Specialized domain experts
+- **Dynamic Router**: Selects relevant encoders per input
+- **Aggregation Layer**: Combines encoder outputs
+- **Mamba Decoder**: Generates final responses
+
+## Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+from mamba_swarm import MambaSwarmEngine
+
+# Load the model
+model = MambaSwarmEngine.from_pretrained("your-username/mamba-swarm-model")
+tokenizer = AutoTokenizer.from_pretrained("your-username/mamba-swarm-model")
+
+# Generate text
+input_text = "Explain quantum computing"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training
+
+This model uses a three-phase training approach:
+1. Collective pre-training on general data
+2. Domain specialization for encoder groups  
+3. End-to-end coordination training
+
+## Performance
+
+- **Parameters**: ~7B total (100 × 70M encoders)
+- **Domains**: Medical, Legal, Code, Science, General
+- **Routing Efficiency**: Only 10-20% of encoders active per query
+
+## Citation
+
+```
+@misc{mamba-swarm-2025,
+  title={Mamba Swarm: Dynamic Routing for Efficient Language Modeling},
+  author={Your Name},
+  year={2025}
+}
+```
+"""
+
+def create_model_config():
+    config = {
+        "model_type": "mamba_swarm",
+        "architectures": ["MambaSwarmForCausalLM"],
+        "num_encoders": 100,
+        "encoder_config": {
+            "d_model": 768,
+            "n_layer": 24,
+            "vocab_size": 50280,
+            "ssm_cfg": {},
+            "rms_norm": True,
+            "residual_in_fp32": True,
+            "fused_add_norm": True
+        },
+        "router_config": {
+            "top_k": 10,
+            "routing_strategy": "content_based"
+        },
+        "aggregator_config": {
+            "method": "weighted_sum",
+            "attention_heads": 8
+        },
+        "torch_dtype": "float16",
+        "use_cache": True
+    }
+    return json.dumps(config, indent=2)
+
+def create_requirements():
+    return """torch>=2.0.0
+transformers>=4.35.0
+mamba-ssm>=1.2.0
+causal-conv1d>=1.2.0
+numpy>=1.21.0
+scipy>=1.7.0
+triton>=2.0.0
+einops>=0.6.1
+packaging>=20.0
+"""
+
+def create_modeling_file():
+    return """# modeling_mamba_swarm.py - HuggingFace integration
+
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.modeling_outputs import CausalLMOutputWithPast
+import torch
+import torch.nn as nn
+
+class MambaSwarmConfig(PretrainedConfig):
+    model_type = "mamba_swarm"
+    
+    def __init__(
+        self,
+        num_encoders=100,
+        encoder_config=None,
+        router_config=None,
+        aggregator_config=None,
+        **kwargs
+    ):
+        self.num_encoders = num_encoders
+        self.encoder_config = encoder_config or {}
+        self.router_config = router_config or {}
+        self.aggregator_config = aggregator_config or {}
+        super().__init__(**kwargs)
+
+class MambaSwarmForCausalLM(PreTrainedModel):
+    config_class = MambaSwarmConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        
+        # Import your actual implementation
+        from mamba_swarm.system.swarm_engine import MambaSwarmEngine
+        
+        self.swarm_engine = MambaSwarmEngine(config)
+        
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        labels=None,
+        **kwargs
+    ):
+        # Your forward pass implementation
+        outputs = self.swarm_engine(input_ids, attention_mask)
+        
+        loss = None
+        if labels is not None:
+            # Calculate loss
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(outputs.logits.view(-1, outputs.logits.size(-1)), labels.view(-1))
+        
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=outputs.logits,
+            past_key_values=outputs.past_key_values,
+        )
+    
+    def generate(self, *args, **kwargs):
+        return self.swarm_engine.generate(*args, **kwargs)
+    
+    @classmethod
+    def from_pretrained(cls, model_name_or_path, *model_args, **kwargs):
+        # Custom loading logic if needed
+        return super().from_pretrained(model_name_or_path, *model_args, **kwargs)
+"""
+
+def upload_model():
+    """Upload model code to HuggingFace"""
+    api = HfApi()
+    
+    # Upload model repository
+    upload_folder(
+        folder_path="hf_model_repo",
+        repo_id="your-username/mamba-swarm-model",  # Replace with your username
+        repo_type="model",
+        commit_message="Initial upload of Mamba Swarm model"
+    )
+    
+    print("Model uploaded successfully!")
+
+def upload_weights():
+    """Upload model weights separately"""
+    # This assumes you have trained weights in checkpoints/
+    api = HfApi()
+    
+    upload_folder(
+        folder_path="checkpoints",
+        repo_id="your-username/mamba-swarm-weights",  # Replace with your username
+        repo_type="model", 
+        commit_message="Upload trained model weights"
+    )
+    
+    print("Weights uploaded successfully!")
+
+if __name__ == "__main__":
+    prepare_model_repo()
+    upload_model()
+    # upload_weights()  # Uncomment when you have trained weights