Upload 15 files

config.json

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+{
+  "architectures": [
+    "Starcoder2ForCausalLM"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "dtype": "bfloat16",
+  "embedding_dropout": 0.1,
+  "eos_token_id": 0,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 3072,
+  "initializer_range": 0.018042,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 16384,
+  "mlp_type": "default",
+  "model_type": "starcoder2",
+  "norm_epsilon": 1e-05,
+  "norm_type": "layer_norm",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 2,
+  "residual_dropout": 0.1,
+  "rope_scaling": null,
+  "rope_theta": 999999.4420358813,
+  "sliding_window": 4096,
+  "transformers_version": "4.57.2",
+  "use_bias": true,
+  "use_cache": true,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.57.2"
+}

gitattributes

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handler (2).py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import json
+import os
+import logging
+import time
+import traceback
+import psutil
+from datetime import datetime
+
+# Set up comprehensive logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - [%(funcName)s:%(lineno)d] - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+
+# Add request tracking
+request_counter = 0
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        """
+        Initialize the handler for Hugging Face Inference Endpoints
+        
+        Args:
+            path (str): Path to the model directory
+        """
+        init_start_time = time.time()
+        logger.info("🚀 Initializing Streamlit Copilot Handler")
+        
+        # Device setup
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        logger.info(f"Device: {self.device}")
+        
+        try:
+            # Check if this is a merged model or LoRA adapter
+            adapter_config_path = os.path.join(path, "adapter_config.json")
+            
+            if os.path.exists(adapter_config_path):
+                logger.info("Loading LoRA adapter model")
+                
+                # This is a LoRA adapter - load base model and adapter
+                base_model_name = "bigcode/starcoder2-3b"
+                
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    base_model_name,
+                    trust_remote_code=True,
+                    use_fast=True
+                )
+                
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    base_model_name,
+                    torch_dtype=torch.float16,
+                    device_map="auto",
+                    trust_remote_code=True,
+                    low_cpu_mem_usage=True
+                )
+                
+                # Load the LoRA adapter
+                self.model = PeftModel.from_pretrained(self.model, path)
+                logger.info("LoRA adapter loaded")
+                
+            else:
+                logger.info("Loading merged model")
+                
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    path,
+                    trust_remote_code=True,
+                    use_fast=True
+                )
+                
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    path,
+                    torch_dtype=torch.float16,
+                    device_map="auto",
+                    trust_remote_code=True,
+                    low_cpu_mem_usage=True
+                )
+                logger.info("Merged model loaded")
+            
+            # Configure tokenizer
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+            
+            # Set model to evaluation mode
+            self.model.eval()
+            
+            if self.device == "cuda":
+                torch.cuda.empty_cache()
+            
+            init_total_time = time.time() - init_start_time
+            logger.info(f"✅ Model initialization completed in {init_total_time:.2f}s")
+            
+        except Exception as e:
+            logger.error(f"❌ Model initialization failed: {str(e)}")
+            logger.error(traceback.format_exc())
+            raise
+
+    def handle_openai_completions(self, data):
+        """
+        Handle OpenAI-style /v1/completions requests for Continue VS Code extension
+        """
+        global request_counter
+        request_counter += 1
+        req_id = f"openai-comp-{request_counter}"
+        start_time = time.time()
+        
+        logger.info(f"[{req_id}] OpenAI Completions request")
+        
+        try:
+            prompt = data.get("prompt", "")
+            if not prompt:
+                logger.warning(f"[{req_id}] No prompt provided")
+                return {"error": {"message": "No prompt provided", "type": "invalid_request"}}
+            
+            logger.info(f"[{req_id}] Prompt: {len(prompt)} chars - {prompt[:50]}{'...' if len(prompt) > 50 else ''}")
+            
+            max_tokens = min(data.get("max_tokens", 100), 512)
+            temperature = max(0.0, min(data.get("temperature", 0.2), 2.0))
+            top_p = max(0.0, min(data.get("top_p", 1.0), 1.0))
+            stop = data.get("stop", [])
+            
+            generated_text = self._generate_text_internal(prompt, max_tokens, temperature, top_p, stop, req_id)
+            
+            response = {
+                "id": f"cmpl-{datetime.now().strftime('%Y%m%d%H%M%S')}",
+                "object": "text_completion",
+                "created": int(datetime.now().timestamp()),
+                "model": "starcoder2-3b-streamlit-copilot",
+                "choices": [{
+                    "text": generated_text,
+                    "index": 0,
+                    "logprobs": None,
+                    "finish_reason": "stop"
+                }]
+            }
+            
+            total_time = time.time() - start_time
+            logger.info(f"[{req_id}] ✅ Completed in {total_time:.2f}s - Generated {len(generated_text)} chars")
+            
+            return response
+            
+        except Exception as e:
+            total_time = time.time() - start_time
+            logger.error(f"[{req_id}] ❌ Failed after {total_time:.2f}s: {str(e)}")
+            return {"error": {"message": str(e), "type": "server_error"}}
+    
+    def handle_openai_chat_completions(self, data):
+        """
+        Handle OpenAI-style /v1/chat/completions requests
+        """
+        global request_counter
+        request_counter += 1
+        req_id = f"openai-chat-{request_counter}"
+        start_time = time.time()
+        
+        logger.info(f"[{req_id}] Chat Completions request")
+        
+        try:
+            messages = data.get("messages", [])
+            if not messages:
+                logger.warning(f"[{req_id}] No messages provided")
+                return {"error": {"message": "No messages provided", "type": "invalid_request"}}
+            
+            logger.info(f"[{req_id}] {len(messages)} messages")
+            
+            # Convert messages to prompt
+            prompt = self._messages_to_prompt(messages)
+            
+            max_tokens = min(data.get("max_tokens", 100), 512)
+            temperature = max(0.0, min(data.get("temperature", 0.2), 2.0))
+            top_p = max(0.0, min(data.get("top_p", 1.0), 1.0))
+            stop = data.get("stop", [])
+            
+            generated_text = self._generate_text_internal(prompt, max_tokens, temperature, top_p, stop, req_id)
+            
+            response = {
+                "id": f"chatcmpl-{datetime.now().strftime('%Y%m%d%H%M%S')}",
+                "object": "chat.completion",
+                "created": int(datetime.now().timestamp()),
+                "model": "starcoder2-3b-streamlit-copilot",
+                "choices": [{
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": generated_text
+                    },
+                    "finish_reason": "stop"
+                }]
+            }
+            
+            total_time = time.time() - start_time
+            logger.info(f"[{req_id}] ✅ Completed in {total_time:.2f}s - Generated {len(generated_text)} chars")
+            
+            return response
+            
+        except Exception as e:
+            total_time = time.time() - start_time
+            logger.error(f"[{req_id}] ❌ Failed after {total_time:.2f}s: {str(e)}")
+            return {"error": {"message": str(e), "type": "server_error"}}
+    
+    def _messages_to_prompt(self, messages):
+        """Convert OpenAI chat messages to a single prompt for code completion"""
+        prompt_parts = []
+        for message in messages:
+            role = message.get("role", "")
+            content = message.get("content", "")
+            
+            if role == "system":
+                prompt_parts.append(f"# {content}")
+            elif role == "user":
+                prompt_parts.append(content)
+            elif role == "assistant":
+                prompt_parts.append(content)
+        
+        return "\n".join(prompt_parts)
+    
+    def _generate_text_internal(self, prompt, max_tokens, temperature, top_p, stop_sequences, req_id="unknown"):
+        """Internal method for text generation"""
+        gen_start_time = time.time()
+        logger.info(f"[{req_id}] Generating text...")
+        
+        try:
+            # Tokenize input
+            input_ids = self.tokenizer.encode(
+                prompt, 
+                return_tensors="pt",
+                truncation=True,
+                max_length=2048
+            ).to(self.device)
+            
+            input_length = input_ids.shape[1]
+            logger.info(f"[{req_id}] Input tokens: {input_length}")
+            
+            # Generate response
+            
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    input_ids,
+                    max_new_tokens=max_tokens,
+                    temperature=temperature if temperature > 0 else 0.1,
+                    do_sample=temperature > 0,
+                    top_p=top_p,
+                    top_k=50,
+                    pad_token_id=self.tokenizer.eos_token_id,
+                    eos_token_id=self.tokenizer.eos_token_id,
+                    repetition_penalty=1.1,
+                    early_stopping=True,
+                    use_cache=True
+                )
+            
+            generation_time = time.time() - gen_start_time
+            new_tokens = outputs.shape[1] - input_length
+            
+            # Decode the response
+            generated_text = self.tokenizer.decode(
+                outputs[0][input_ids.shape[1]:], 
+                skip_special_tokens=True
+            ).strip()
+            
+            logger.info(f"[{req_id}] Generated {new_tokens} tokens ({new_tokens/generation_time:.1f} t/s)")
+            
+            # Apply stop sequences
+            if stop_sequences:
+                for stop_seq in stop_sequences:
+                    if stop_seq in generated_text:
+                        generated_text = generated_text.split(stop_seq)[0]
+                        break
+            
+            logger.info(f"[{req_id}] Generated text: {generated_text[:100]}{'...' if len(generated_text) > 100 else ''}")
+            
+            return generated_text
+            
+        except Exception as e:
+            total_gen_time = time.time() - gen_start_time
+            logger.error(f"[{req_id}] Generation failed after {total_gen_time:.2f}s: {str(e)}")
+            raise
+
+    def __call__(self, data):
+        """
+        Main inference method - supports both HuggingFace and OpenAI API formats
+        
+        Args:
+            data (dict): The object received by the inference server
+                Continue/OpenAI format:
+                {
+                    "inputs": "Your code prompt here", 
+                    "stream": true,
+                    "parameters": {
+                        "max_new_tokens": 100,
+                        "temperature": 0.7,
+                        "top_p": 0.9
+                    }
+                }
+                OpenAI Completions format:
+                {
+                    "prompt": "Your code prompt here",
+                    "max_tokens": 100,
+                    "temperature": 0.2
+                }
+                OpenAI Chat format:
+                {
+                    "messages": [...],
+                    "max_tokens": 100
+                }
+        Returns:
+            dict: Generated response with metadata
+        """
+        global request_counter
+        request_counter += 1
+        req_start_time = time.time()
+        
+        logger.info(f"Request #{request_counter} - Keys: {list(data.keys())}")
+        
+        # Detect request format and route accordingly
+        if "messages" in data:
+            # OpenAI Chat Completions format
+            result = self.handle_openai_chat_completions(data)
+        elif "prompt" in data:
+            # OpenAI Completions format  
+            result = self.handle_openai_completions(data)
+        elif "inputs" in data and ("stream" in data or any(key in data for key in ["parameters", "temperature", "max_tokens"])):
+            # Continue VS Code extension format - return OpenAI format for llama.cpp/openai providers  
+            req_id = f"continue-{request_counter}"
+            logger.info(f"[{req_id}] Continue HuggingFace-TGI compatible request")
+            
+            try:
+                inputs = data.get("inputs", "")
+                if not inputs:
+                    logger.warning(f"[{req_id}] No inputs provided")
+                    return {"error": {"message": "No input text provided", "type": "invalid_request"}}
+                
+                logger.info(f"[{req_id}] Input: {len(inputs)} chars - {inputs[:50]}{'...' if len(inputs) > 50 else ''}")
+                
+                # Extract parameters (Continue uses HF-style parameters)
+                parameters = data.get("parameters", {})
+                max_new_tokens = min(parameters.get("max_new_tokens", data.get("max_tokens", 150)), 512)
+                temperature = max(0.0, min(parameters.get("temperature", data.get("temperature", 0.2)), 2.0))
+                top_p = max(0.0, min(parameters.get("top_p", data.get("top_p", 1.0)), 1.0))
+                stop = data.get("stop", parameters.get("stop", []))
+                
+                # Generate text
+                generated_text = self._generate_text_internal(
+                    inputs, max_new_tokens, temperature, top_p, stop, req_id
+                )
+                
+                # Return HuggingFace format for Continue huggingface-tgi provider
+                result = [{
+                    "generated_text": generated_text
+                }]
+                
+            except Exception as e:
+                total_time = time.time() - req_start_time
+                logger.error(f"[{req_id}] Failed after {total_time:.2f}s: {str(e)}")
+                result = {"error": {"message": str(e), "type": "server_error"}}
+        
+        else:
+            # Legacy HuggingFace format (pure HF testing)
+            req_id = f"hf-{request_counter}"
+            logger.info(f"[{req_id}] Legacy HF format request")
+            
+            try:
+                inputs = data.get("inputs", "")
+                if not inputs:
+                    logger.warning(f"[{req_id}] No inputs provided")
+                    return {"error": "No input text provided"}
+                
+                logger.info(f"[{req_id}] Input: {len(inputs)} chars - {inputs[:50]}{'...' if len(inputs) > 50 else ''}")
+                
+                parameters = data.get("parameters", {})
+                
+                # Validate and set generation parameters
+                max_new_tokens = min(parameters.get("max_new_tokens", 150), 512)
+                temperature = max(0.1, min(parameters.get("temperature", 0.7), 1.0))
+                top_p = max(0.1, min(parameters.get("top_p", 0.9), 1.0))
+                
+                # Use internal generation method
+                generated_text = self._generate_text_internal(
+                    inputs, max_new_tokens, temperature, top_p, [], req_id
+                )
+                
+                # Return response in HF Inference Endpoint format
+                result = [{
+                    "generated_text": generated_text
+                }]
+                
+            except Exception as e:
+                total_time = time.time() - req_start_time
+                logger.error(f"[{req_id}] Failed after {total_time:.2f}s: {str(e)}")
+                result = {"error": f"Generation failed: {str(e)}"}
+        
+        # Final request logging
+        total_request_time = time.time() - req_start_time
+        
+        if isinstance(result, dict) and "error" in result:
+            logger.error(f"Request #{request_counter} ❌ Failed in {total_request_time:.2f}s")
+        else:
+            logger.info(f"Request #{request_counter} ✅ Completed in {total_request_time:.2f}s")
+        
+        return result

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+}

special_tokens_map.json

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+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,358 @@
+{
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