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.gitattributes

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-*.pt filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+model.* filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.py text diff=python
+*.meta-huggingface.json text

.huggingface.co/repository-structure.json

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+{
+    "repos": {
+        "*": {
+            "stripe_disabled": false,
+            "files-tracking": {
+                "patterns": [
+                    "**/*.pt",
+                    "**/*.bin",
+                    "**/*.pkl",
+                    "**/*.pth",
+                    "**/*.ckpt",
+                    "**/*.safetensors",
+                    "**/*.onnx",
+                    "**/model.*",
+                    "**/*.h5",
+                    "**/*.py",
+                    "**/*.meta-huggingface.json"
+                ],
+                "ignore_patterns": []
+            }
+        }
+    }
+}

checkpoints/best_model.pt

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checkpoints/checkpoint_epoch_1.pt

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checkpoints/checkpoint_epoch_2.pt

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checkpoints/checkpoint_epoch_3.pt

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src/model.py

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-import torch
-from .decent_torch import DecentModel
-from .openpeer import OpenPeerClient
-from .grammar import LonScriptGrammar
-from .modeling_openpeer import OpenPeerLLM
-from .configuration_openpeer import OpenPeerConfig
-from .tokenization_openpeer import OpenPeerTokenizer
-import asyncio
-from typing import Dict, Any, Optional
-
-class DecentralizedLLM(DecentModel):
-    def __init__(self, network_url: str = "ws://localhost:8000"):
-        super().__init__()
-        # Initialize our custom LLM
-        self.config = OpenPeerConfig()
-        self.model = OpenPeerLLM(self.config)
-        self.tokenizer = OpenPeerTokenizer()
-        self.peer_client = OpenPeerClient(network_url)
-        self.grammar = LonScriptGrammar()
-        self._ensure_model_on_device()
-        
-    def _ensure_model_on_device(self):
-        """Ensure model is on the correct device"""
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model = self.model.to(device)
-        
-    def forward(self, input_text: str) -> str:
-        # Tokenize input
-        inputs = self.tokenizer(input_text, return_tensors="pt")
-        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
-        
-        # Generate response using our custom LLM
-        with torch.no_grad():
-            outputs = self.model(**inputs)
-            logits = outputs["logits"]
-            
-            # Get next token predictions
-            next_token_logits = logits[:, -1, :]
-            next_tokens = torch.argmax(next_token_logits, dim=-1)
-            
-            generated_ids = [inputs["input_ids"][0].tolist()]
-            for _ in range(100):  # max length
-                curr_input = torch.tensor([generated_ids[-1]], device=self.model.device)
-                with torch.no_grad():
-                    outputs = self.model(curr_input)
-                    next_token_logits = outputs["logits"][:, -1, :]
-                    next_token = torch.argmax(next_token_logits, dim=-1).item()
-                    
-                generated_ids.append([next_token])
-                
-                if next_token == self.tokenizer.eos_token_id:
-                    break
-        
-        # Decode and return results
-        decoded_output = self.tokenizer.decode(torch.tensor(generated_ids).flatten(), skip_special_tokens=True)
-        return decoded_output
-from .grammar import LonScriptGrammar
-from .modeling_openpeer import OpenPeerLLM
-from .configuration_openpeer import OpenPeerConfig
-from .tokenization_openpeer import OpenPeerTokenizer
-import asyncio
-from typing import Dict, Any, Optional
-
-class DecentralizedLLM(DecentModel):
-    def __init__(self, network_url: str = "ws://localhost:8000"):
-        super().__init__()
-        # Initialize our custom LLM
-        self.config = OpenPeerConfig()
-        self.model = OpenPeerLLM(self.config)
-        self.tokenizer = OpenPeerTokenizer()
-        self.peer_client = OpenPeerClient(network_url)
-        self.grammar = LonScriptGrammar()
-        self._ensure_model_on_device()
-        
-    def _ensure_model_on_device(self):
-        """Ensure model is on the correct device"""
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model = self.model.to(device)
-        
-    async def connect_to_network(self):
-        """Connect to the peer network"""
-        await self.peer_client.connect(self.peer_id)
-        asyncio.create_task(self._handle_peer_updates())
-        
-    async def _handle_peer_updates(self):
-        """Handle incoming updates from peers"""
-        async for update in self.peer_client.receive_updates():
-            if update["type"] == "model_update":
-                await self._process_model_update(update)
-                
-    async def _process_model_update(self, update: Dict[str, Any]):
-        """Process received model updates"""
-        state_dict = {k: torch.tensor(v) for k, v in update["state"].items()}
-        self.state_updates[update["peer_id"]] = state_dict
-        self.aggregate_states()
-        
-    def forward(self, input_text: str) -> str:
-        """Generate response for input text"""
-        # Tokenize input
-        inputs = self.tokenizer(input_text, return_tensors="pt")
-        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
-        
-        # Generate response
-        with torch.no_grad():
-            outputs = self.model.generate(
-                **inputs,
-                max_length=100,
-                num_return_sequences=1,
-                pad_token_id=self.tokenizer.eos_token_id
-            )
-        
-        # Decode and return results
-        decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return decoded_output
-        
-    async def train_step(self, batch: Dict[str, torch.Tensor]):
-        """Perform a training step and share updates with peers"""
-        # Forward pass
-        outputs = self.model(**batch)
-        loss = outputs.loss
-        
-        # Backward pass
-        loss.backward()
-        
-        # Optimizer step would go here
-        # self.optimizer.step()
-        
-        # Share updated model state with peers
-        await self.peer_client.send_model_update(self.model.state_dict())
-        
-    def reason(self, context: str, query: str) -> str:
-        """Implement deep reasoning capabilities with grammar enhancement"""
-        # Combine context and query
-        prompt = f"Context: {context}\nQuery: {query}\nReasoned response:"
-        
-        # Generate initial response
-        initial_response = self.forward(prompt)
-        
-        # Apply grammar rules for enhanced understanding
-        enhanced_response = self.grammar.apply_grammar_rules(initial_response)
-        
-        return enhanced_response
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