Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_cache/models--mistralai--Mistral-7B-Instruct-v0.3/blobs/37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89 filter=lfs diff=lfs merge=lfs -text
+model_cache/models--mistralai--Mistral-7B-Instruct-v0.3/blobs/8c0e72f148366b6a3709e002a98706a33d31aec8515090c856c95b2044f92ae0 filter=lfs diff=lfs merge=lfs -text
+model_cache/models--mistralai--Mistral-7B-Instruct-v0.3/blobs/905dd405363e43d95779c1c1155a2dbfd36155914ae95dbd934e12e490cfb4ca filter=lfs diff=lfs merge=lfs -text
+model_cache/models--mistralai--Mistral-7B-Instruct-v0.3/blobs/ce6fb6f6f4d0183f4813cbf4ece24109da629a08d4210da46f77e1d8b0bd5c19 filter=lfs diff=lfs merge=lfs -text

adapter_config.json

@@ -30,5 +30,6 @@
   "task_type": "CAUSAL_LM",
   "trainable_token_indices": null,
   "use_dora": false,
-  "use_rslora": false
+  "use_rslora": false,
+  "model_type": "mistral"
 }

config.json

@@ -0,0 +1,34 @@
+{
+    "model_type": "mistral",
+    "architectures": ["MistralForCausalLM"],
+    "base_model": "mistralai/Mistral-7B-Instruct-v0.3",
+    "hidden_size": 4096,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "rms_norm_eps": 1e-5,
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float16",
+    "use_cache": true,
+    "vocab_size": 32000,
+    "adapter_config": {
+        "r": 16,
+        "lora_alpha": 32,
+        "target_modules": ["q_proj", "v_proj"],
+        "lora_dropout": 0.1,
+        "bias": "none",
+        "task_type": "CAUSAL_LM"
+    },
+    "training_config": {
+        "checkpoint_latest": "checkpoint-36",
+        "total_steps": 36,
+        "optimizer": "AdamW",
+        "learning_rate": 5e-5,
+        "warmup_steps": 100,
+        "weight_decay": 0.01
+    }
+}

fix_interference_api.py

@@ -0,0 +1,78 @@
+import os
+import json
+import yaml
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import logging
+import time
+
+# Add more detailed logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_model_loading(base_model='mistralai/Mistral-7B-Instruct-v0.3', timeout=600):
+    try:
+        start_time = time.time()
+        
+        # Log more details about download process
+        logger.info(f"Attempting to load tokenizer from {base_model}")
+        tokenizer = AutoTokenizer.from_pretrained(
+            base_model, 
+            # Add cache directory explicitly
+            cache_dir='./model_cache',
+            # Add timeout parameters
+            use_auth_token=False,
+            local_files_only=False,
+            resume_download=True
+        )
+        
+        logger.info(f"Tokenizer loaded. Setting pad token if needed.")
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        # Log model loading with more parameters
+        logger.info(f"Loading model with extended timeout handling")
+        model = AutoModelForCausalLM.from_pretrained(
+            base_model, 
+            cache_dir='./model_cache',
+            use_auth_token=False,
+            local_files_only=False,
+            resume_download=True,
+            # Optional: Force download if needed
+            force_download=False,
+            # Optional: Use torch compile for potential performance
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+        )
+        
+        # Rest of the generation code remains the same
+        input_text = "Hello, how are you today?"
+        inputs = tokenizer(
+            input_text, 
+            return_tensors="pt", 
+            padding=True, 
+            add_special_tokens=True,
+            return_attention_mask=True
+        )
+        
+        with torch.no_grad():
+            output = model.generate(
+                input_ids=inputs['input_ids'], 
+                attention_mask=inputs['attention_mask'],
+                max_length=20,
+                num_return_sequences=1,
+                do_sample=False
+            )
+        
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        logger.info(f"Generation successful: {generated_text}")
+        
+        return True
+    
+    except Exception as e:
+        logger.error(f"Detailed error during model loading: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == '__main__':
+    test_model_loading()

mistral-config-diagnostics.py

@@ -0,0 +1,78 @@
+import os
+import json
+import yaml
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import logging
+import time
+
+# Add more detailed logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_model_loading(base_model='mistralai/Mistral-7B-Instruct-v0.3', timeout=600):
+    try:
+        start_time = time.time()
+        
+        # Log more details about download process
+        logger.info(f"Attempting to load tokenizer from {base_model}")
+        tokenizer = AutoTokenizer.from_pretrained(
+            base_model, 
+            # Add cache directory explicitly
+            cache_dir='./model_cache',
+            # Add timeout parameters
+            use_auth_token=False,
+            local_files_only=False,
+            resume_download=True
+        )
+        
+        logger.info(f"Tokenizer loaded. Setting pad token if needed.")
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        # Log model loading with more parameters
+        logger.info(f"Loading model with extended timeout handling")
+        model = AutoModelForCausalLM.from_pretrained(
+            base_model, 
+            cache_dir='./model_cache',
+            use_auth_token=False,
+            local_files_only=False,
+            resume_download=True,
+            # Optional: Force download if needed
+            force_download=False,
+            # Optional: Use torch compile for potential performance
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+        )
+        
+        # Rest of the generation code remains the same
+        input_text = "Hello, how are you today?"
+        inputs = tokenizer(
+            input_text, 
+            return_tensors="pt", 
+            padding=True, 
+            add_special_tokens=True,
+            return_attention_mask=True
+        )
+        
+        with torch.no_grad():
+            output = model.generate(
+                input_ids=inputs['input_ids'], 
+                attention_mask=inputs['attention_mask'],
+                max_length=20,
+                num_return_sequences=1,
+                do_sample=False
+            )
+        
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        logger.info(f"Generation successful: {generated_text}")
+        
+        return True
+    
+    except Exception as e:
+        logger.error(f"Detailed error during model loading: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == '__main__':
+    test_model_loading()

mistral-config.json

@@ -0,0 +1,34 @@
+{
+    "model_type": "mistral",
+    "architectures": ["MistralForCausalLM"],
+    "base_model": "mistralai/Mistral-7B-Instruct-v0.3",
+    "hidden_size": 4096,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "rms_norm_eps": 1e-5,
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float16",
+    "use_cache": true,
+    "vocab_size": 32000,
+    "adapter_config": {
+        "r": 16,
+        "lora_alpha": 32,
+        "target_modules": ["q_proj", "v_proj"],
+        "lora_dropout": 0.1,
+        "bias": "none",
+        "task_type": "CAUSAL_LM"
+    },
+    "training_config": {
+        "checkpoint_latest": "checkpoint-36",
+        "total_steps": 36,
+        "optimizer": "AdamW",
+        "learning_rate": 5e-5,
+        "warmup_steps": 100,
+        "weight_decay": 0.01
+    }
+}

mistral-model-readiness-checker.py

@@ -0,0 +1,149 @@
+import os
+import json
+import yaml
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import HfApi
+
+def load_json_config(file_path):
+    """Safely load JSON configuration files"""
+    try:
+        with open(file_path, 'r') as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"❌ Error loading {file_path}: {e}")
+        return None
+
+def review_adapter_config(config_path):
+    """
+    Comprehensive review of adapter configuration
+    """
+    print("\n🔍 Adapter Configuration Analysis:")
+    config = load_json_config(config_path)
+    
+    if not config:
+        return
+    
+    # Key checks for adapter configuration
+    checks = [
+        ("Adapter Type", config.get('adapter_type')),
+        ("Base Model", config.get('base_model_name_or_path')),
+        ("Reduction Factor", config.get('reduction_factor')),
+        ("Target Modules", config.get('target_modules'))
+    ]
+    
+    for label, value in checks:
+        status = "✅" if value is not None else "❗"
+        print(f"{status} {label}: {value}")
+    
+    # Additional insights
+    if 'peft_type' in config:
+        print(f"✅ PEFT Type: {config['peft_type']}")
+
+def review_tokenizer_config(config_path):
+    """
+    Comprehensive review of tokenizer configuration
+    """
+    print("\n🔍 Tokenizer Configuration Analysis:")
+    config = load_json_config(config_path)
+    
+    if not config:
+        return
+    
+    # Tokenizer key checks
+    tokenizer_checks = [
+        ("Vocabulary Size", config.get('vocab_size')),
+        ("Padding Side", config.get('padding_side')),
+        ("Truncation Side", config.get('truncation_side')),
+        ("Model Max Length", config.get('model_max_length')),
+        ("Special Tokens", config.get('special_tokens_map_file'))
+    ]
+    
+    for label, value in tokenizer_checks:
+        status = "✅" if value is not None else "❗"
+        print(f"{status} {label}: {value}")
+
+def check_inference_yaml(yaml_path):
+    """
+    Review inference configuration
+    """
+    print("\n🚀 Inference Configuration Analysis:")
+    try:
+        with open(yaml_path, 'r') as f:
+            inference_config = yaml.safe_load(f)
+        
+        # Check key inference parameters
+        print("Inference Configuration Details:")
+        print(json.dumps(inference_config, indent=2))
+    except Exception as e:
+        print(f"❌ Error reading inference YAML: {e}")
+
+def model_loading_test(model_id):
+    """
+    Test model loading and basic generation
+    """
+    print("\n🧪 Model Loading and Generation Test:")
+    try:
+        # Load tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        print("✅ Tokenizer Loaded Successfully")
+        
+        # Load model
+        model = AutoModelForCausalLM.from_pretrained(model_id)
+        print("✅ Model Loaded Successfully")
+        
+        # Basic generation test
+        test_prompt = "Explain machine learning in simple terms:"
+        input_ids = tokenizer.encode(test_prompt, return_tensors="pt")
+        
+        # Generate response
+        output = model.generate(input_ids, max_length=100, num_return_sequences=1)
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        
+        print("✅ Basic Generation Test Passed")
+        print("\n📝 Generated Sample:")
+        print(generated_text)
+    except Exception as e:
+        print(f"❌ Model Loading/Generation Failed: {e}")
+
+def optimize_repository_structure(model_id):
+    """
+    Provide recommendations for repository optimization
+    """
+    print("\n🛠️ Repository Optimization Recommendations:")
+    
+    # Checkpoint management
+    print("Checkpoint Management:")
+    print("1. Consider keeping only the latest checkpoint (36)")
+    print("2. Archive or remove older checkpoints to reduce repository size")
+    
+    # Configuration file cleanup
+    print("\nConfiguration File Optimization:")
+    print("1. Ensure consistent naming across checkpoint configurations")
+    print("2. Verify that the latest checkpoint has the most up-to-date configs")
+    
+    # Metadata and documentation
+    print("\nDocumentation Recommendations:")
+    print("1. Update README.md with:")
+    print("   - Model architecture details")
+    print("   - Training methodology")
+    print("   - Intended use cases")
+    print("   - Performance metrics")
+
+def main():
+    model_id = "mycholpath/RA-Mistral-7B"
+    base_path = "."  # Adjust if needed
+    
+    # Configuration file paths
+    adapter_config_path = os.path.join(base_path, "adapter_config.json")
+    tokenizer_config_path = os.path.join(base_path, "tokenizer_config.json")
+    inference_yaml_path = os.path.join(base_path, "inference.yaml")
+    
+    # Run comprehensive diagnostics
+    review_adapter_config(adapter_config_path)
+    review_tokenizer_config(tokenizer_config_path)
+    check_inference_yaml(inference_yaml_path)
+    model_loading_test(model_id)
+    optimize_repository_structure(model_id)
+
+if __name__ == "__main__":
+    main()

model_cache/models--mistralai--Mistral-7B-Instruct-v0.3/blobs/050e5515d49376fc01e4837d031902c63af98111

@@ -0,0 +1,298 @@
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