Upload Multi-Task Address Reasoning model (checkpoint-435)

.gitattributes

@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,391 @@
+# 🧠 Multi-Task Address Reasoning Model v1.0
+
+This model is a **multi-task fine-tuned model** specialized for **address correction, component extraction, and geographic Q&A** with **Chain of Thought reasoning**. Built on Llama-3.2-1B-Instruct with LoRA fine-tuning using Unsloth.
+
+## 🎯 Model Description
+
+Multi-task Llama-3.2-1B model fine-tuned with LoRA for Indian address correction, component extraction, and geographic Q&A using Chain of Thought reasoning
+
+### Key Capabilities
+
+- **🔧 Address Correction**: Fix spelling errors, formatting issues, and incomplete addresses
+- **📊 Component Extraction**: Extract and structure address components (building, locality, city, state, pincode)
+- **❓ Geographic Q&A**: Answer questions about locations, states, cities, and geographic relationships
+- **🧠 Chain of Thought Reasoning**: Detailed step-by-step reasoning for address analysis
+- **🎯 Multi-Task Learning**: Single model handles multiple address-related tasks
+
+## 📊 Model Architecture & Training
+
+- **Base Model**: unsloth/Llama-3.2-1B-Instruct
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) via Unsloth
+- **LoRA Rank (r)**: 64
+- **LoRA Alpha**: 128
+- **LoRA Dropout**: 0.1
+- **Target Modules**: q_proj, o_proj, k_proj, up_proj, v_proj, down_proj, gate_proj
+- **Model Size**: ~276MB (adapter only)
+- **Checkpoint**: 435
+- **Max Sequence Length**: 1024 tokens (auto-optimized from sequence analysis)
+
+### Training Configuration
+- **Learning Rate**: 1e-4
+- **Batch Size**: 32 (1 per device × 32 gradient accumulation)
+- **Epochs**: 3
+- **Optimizer**: adamw_8bit
+- **Scheduler**: cosine
+- **Weight Decay**: 0.01
+
+## 🚀 Usage Examples
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import warnings
+import json
+warnings.filterwarnings("ignore")
+
+# Load base model and tokenizer (using actual base model from training)
+base_model_name = "unsloth/Llama-3.2-1B-Instruct"  # Actual base model used in training
+model_name = "shiprocket-ai/multitask-address-reasoning-llama-1B-model"
+
+print("📥 Loading tokenizer...")
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Add pad token if missing
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+print("📥 Loading base model...")
+# Load base model (non-quantized version as per training script)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+print("📥 Loading LoRA adapter...")
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, model_name)
+
+print("✅ Model loaded successfully!")
+
+def process_address_with_reasoning(prompt, max_new_tokens=400):
+    """Process address with Chain of Thought reasoning (as trained)"""
+
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+
+    # Move inputs to model device
+    device = next(model.parameters()).device
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    # Generate with reasoning (matching training parameters)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=0.1,  # Lower temperature as used in training testing
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            use_cache=True
+        )
+
+    # Decode only the new tokens
+    input_length = inputs['input_ids'].shape[1]
+    generated_tokens = outputs[0][input_length:]
+    response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+    return response.strip()
+
+def fix_address_with_reasoning(address, max_new_tokens=400):
+    """Fix address with detailed Chain of Thought reasoning"""
+
+    messages = [
+        {"role": "user", "content": f"Fix and extract components from: {address}"}
+    ]
+
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    return process_address_with_reasoning(prompt, max_new_tokens)
+
+def answer_geographic_question(question, max_new_tokens=150):
+    """Answer geographic questions about addresses"""
+
+    messages = [
+        {"role": "user", "content": question}
+    ]
+
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    return process_address_with_reasoning(prompt, max_new_tokens)
+
+def extract_components(address, max_new_tokens=200):
+    """Extract address components with reasoning"""
+
+    messages = [
+        {"role": "user", "content": f"Extract all components from this address: {address}"}
+    ]
+
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    return process_address_with_reasoning(prompt, max_new_tokens)
+
+# Test cases based on training script examples
+print("
+🏠 MULTI-TASK ADDRESS MODEL EXAMPLES")
+print("=" * 60)
+print("🧠 Testing Chain of Thought reasoning + Geographic Q&A")
+print("📊 Model trained with LoRA r=64, alpha=128 for complex reasoning")
+print("=" * 60)
+
+# Test address correction with reasoning (exact example from training)
+test_addresses = [
+    "pandit nagla badi masjid moradabad 244001",
+    "sec 14 gurgoan haryana 122001",
+    "koramangala bangalor 560095",
+    "dlf cyber city gurgaon haryana"
+]
+
+print(f"
+🔧 TESTING ADDRESS CORRECTION WITH CHAIN OF THOUGHT:")
+print("-" * 50)
+
+for i, test_address in enumerate(test_addresses, 1):
+    print(f"
+📍 Test {i}: {test_address}")
+    result = fix_address_with_reasoning(test_address)
+    print(f"🤖 Chain of Thought Response:")
+    print(f"   {result}")
+    print("-" * 40)
+
+# Test geographic Q&A (examples from training script)
+qa_tests = [
+    "Which state is Mumbai in?",
+    "What is the pincode of Bangalore?",
+    "Is Delhi a metro city?",
+    "What tier city is Pune?",
+    "Where is Connaught Place located?",
+    "What state does Hyderabad belong to?",
+    "Name a city in Karnataka.",
+    "What is the postal code for Gurgaon?",
+    "Which state is New Delhi in?",  # Training example
+    "What cities are in Maharashtra?"
+]
+
+print(f"
+❓ TESTING GEOGRAPHIC Q&A:")
+print("-" * 50)
+
+for i, question in enumerate(qa_tests[:8], 1):  # Test first 8 questions
+    print(f"
+❓ Q{i}: {question}")
+    result = answer_geographic_question(question)
+    print(f"🤖 Answer: {result}")
+
+# Test component extraction
+print(f"
+📊 TESTING COMPONENT EXTRACTION:")
+print("-" * 50)
+
+extraction_tests = [
+    "Flat 203, Emerald Heights, Sector 15, Gurugram, Haryana 122001",
+    "DLF Cyber City, Cyber Hub, Gurgaon, Haryana",
+    "Connaught Place, New Delhi, Delhi 110001"
+]
+
+for i, test_address in enumerate(extraction_tests, 1):
+    print(f"
+📊 Extract {i}: {test_address}")
+    result = extract_components(test_address)
+    print(f"🤖 Components: {result}")
+
+print(f"
+✅ ALL TESTS COMPLETED!")
+print(f"🧠 Model demonstrates Chain of Thought reasoning")
+print(f"📍 Geographic knowledge from NER training data")
+print(f"🔧 Address correction with detailed analysis")
+```
+
+## 🧠 Training Methodology
+
+This model was trained using a sophisticated multi-task approach:
+
+### **1. Data Preparation Strategy**
+- **Source**: Address NER dataset with structured components (address → corrected_address → extracted_info)
+- **Multi-task Split**: 70% Chain of Thought address correction + 30% Geographic Q&A
+- **Data Augmentation**: Generated 584.8% of original data from original dataset
+- **Reasoning Integration**: Each sample includes step-by-step analytical reasoning
+
+### **2. Chain of Thought Address Correction**
+- **Input**: Raw/incomplete addresses with potential errors
+- **Process**: Model analyzes, identifies issues, and explains corrections
+- **Output**: Detailed reasoning + structured JSON with address components
+- **Examples**: Spelling fixes, state inference, component extraction
+
+### **3. Geographic Q&A Generation**
+From each address record's NER data, the model generates multiple Q&A pairs:
+- **State-City relationships**: "Which state is Mumbai in?" → "Mumbai is in Maharashtra state."
+- **Pincode queries**: "What is the pincode of Bangalore?" → "The pincode of Bangalore is 560001."
+- **City tier classification**: "Is Delhi a metro city?" → "Yes, Delhi is a metropolitan city."
+- **Locality mapping**: "Where is Connaught Place?" → "Connaught Place is in New Delhi."
+
+### **4. Sequence Optimization**
+- **Dynamic Analysis**: Analyzed 1000+ samples to determine optimal context length
+- **Result**: 99% samples fit in 768 tokens, optimized for 1024
+- **Context Window**: 1024 tokens (auto-optimized from sequence analysis) chosen for reasoning tasks
+
+## 🔧 Training Performance
+
+```
+Final Training Loss: 0.5506
+Training Runtime: 3701.74 seconds (~1 hour)
+Training Samples/Second: 3.749
+Training Steps/Second: 0.118
+Total Epochs: 3.0
+```
+
+## 🎭 Supported Tasks
+
+### 1. **Address Correction with Reasoning**
+- Fix spelling errors and formatting issues
+- Infer missing components (state, city tier)
+- Provide step-by-step reasoning for corrections
+
+### 2. **Component Extraction**
+- Extract building names, localities, cities, states, pincodes
+- Structure unstructured address data
+- Identify address hierarchy and relationships
+
+### 3. **Geographic Q&A**
+- Answer questions about cities, states, and locations
+- Provide geographic knowledge and relationships
+- Handle location-based queries
+
+### 4. **Address Standardization**
+- Convert informal addresses to structured format
+- Normalize address formats
+- Handle various input formats
+
+## 💡 Use Cases
+
+### 1. **E-commerce & Logistics**
+- Correct customer addresses during checkout
+- Extract delivery components for routing
+- Answer location-based customer queries
+
+### 2. **Data Processing & Migration**
+- Clean legacy address databases with reasoning
+- Extract structured data from unstructured addresses
+- Provide explanations for address corrections
+
+### 3. **Customer Support Automation**
+- Answer geographic questions about locations
+- Help customers correct their addresses
+- Provide location-based information
+
+### 4. **Address Intelligence**
+- Analyze address patterns and relationships
+- Infer missing address components
+- Provide geographic context and reasoning
+
+## 🎯 Prompt Formats
+
+The model works with Llama-3.2 chat format:
+
+### Address Correction
+```
+<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+
+Fix and extract components from: [address]<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+```
+
+### Geographic Q&A
+```
+<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+
+Which state is [location] in?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+```
+
+### Component Extraction
+```
+<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+
+Extract all components from this address: [address]<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+```
+
+## ⚡ Performance Tips
+
+1. **Temperature Settings**: Use 0.1-0.3 for factual tasks, 0.3-0.5 for reasoning tasks
+2. **Context Management**: Keep prompts under 512 tokens for optimal performance
+3. **Batch Processing**: Process multiple addresses efficiently with batching
+4. **Device Placement**: Ensure all tensors are on the same device (GPU/CPU)
+5. **Memory Management**: Use float16 for memory efficiency
+
+## ⚠️ Limitations
+
+- **Model Size**: 1B parameters - may have limitations compared to larger models
+- **Training Data**: Based on specific dataset - may not generalize to all address formats
+- **Geographic Scope**: Optimized for Indian addresses and geography
+- **Reasoning Depth**: Chain of thought reasoning may vary in complexity
+- **Device Compatibility**: Requires proper device placement for inference
+
+## 📋 Model Files
+
+- `adapter_config.json`: LoRA adapter configuration
+- `adapter_model.safetensors`: LoRA adapter weights
+- `tokenizer_config.json`: Tokenizer configuration
+- `tokenizer.json`: Tokenizer vocabulary and settings
+- `special_tokens_map.json`: Special tokens mapping
+- `chat_template.jinja`: Chat template for conversations
+
+## 🔄 Model Updates
+
+- **Version**: 1.0 (Checkpoint 435)
+- **Last Updated**: 2025-07-08
+- **Training Framework**: Unsloth + LoRA
+- **Base Model**: Llama-3.2-1B-Instruct
+
+## 📚 Citation
+
+If you use this model in your research or applications, please cite:
+
+```bibtex
+@misc{multitask-address-reasoning-model,
+  title={Multi-Task Address Reasoning Model},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/shiprocket-ai/multitask-address-reasoning-llama-1B-model}
+}
+```
+
+## 📞 Support & Contact
+
+For questions, issues, or feature requests:
+- Open an issue in this repository
+- Contact: shiprocket-ai team
+- Documentation: See usage examples above
+
+## 📜 License
+
+This model is released under the Apache 2.0 License. See LICENSE file for details.
+
+---
+
+*Multi-task address intelligence with reasoning - Built with 🧠 by shiprocket-ai team using Unsloth*

adapter_config.json

@@ -0,0 +1,41 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "o_proj",
+    "k_proj",
+    "up_proj",
+    "v_proj",
+    "down_proj",
+    "gate_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

model_card_metadata.json

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+{
+  "language": [
+    "en"
+  ],
+  "tags": [
+    "address-correction",
+    "multi-task-learning",
+    "chain-of-thought",
+    "geographic-qa",
+    "component-extraction",
+    "text-generation",
+    "llama-3.2",
+    "lora",
+    "unsloth",
+    "address-reasoning",
+    "peft",
+    "instruction-tuned"
+  ],
+  "datasets": [
+    "custom-multi-task-addresses"
+  ],
+  "metrics": [
+    "accuracy",
+    "reasoning-quality"
+  ],
+  "model_type": "causal-lm",
+  "base_model": "unsloth/Llama-3.2-1B-Instruct",
+  "pipeline_tag": "text-generation",
+  "peft": true,
+  "widget": [
+    {
+      "text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n\\nFix and extract components from: pandit nagla badi masjid moradabad 244001<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nAnalyzing the address:\\n- Fixed spelling/formatting\\n- Inferred state: Uttar Pradesh (from Moradabad city)\\n\\nResult: {\\\"building\\\": \\\"Pandit Nagla Badi Masjid\\\", \\\"city\\\": \\\"Moradabad\\\", \\\"state\\\": \\\"Uttar Pradesh\\\", \\\"pincode\\\": \\\"244001\\\"}",
+      "example_title": "Address Correction with Reasoning"
+    },
+    {
+      "text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n\\nWhich state is New Delhi in?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n",
+      "example_title": "Geographic Q&A"
+    }
+  ]
+}

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+torch>=2.0.0
+transformers>=4.35.0
+peft>=0.6.0
+bitsandbytes>=0.41.0
+accelerate>=0.24.0

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+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
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+{
+  "add_bos_token": true,
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
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