Upload fine-tuned Phi-3 reverse payments model (NL → Structured)

README.md

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+---
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+tags:
+- phi-3
+- lora
+- payments
+- finance
+- information-extraction
+- structured-data-extraction
+- text-to-data
+- finetuned
+datasets:
+- custom
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Phi-3 Mini Reverse Fine-tuned for Payments Domain
+
+This is a **reverse** fine-tuned version of [Microsoft's Phi-3-Mini-4k-Instruct](microsoft/Phi-3-mini-4k-instruct) model, adapted for extracting structured payment metadata from natural language descriptions using LoRA (Low-Rank Adaptation).
+
+## Model Description
+
+This model converts natural language payment descriptions into structured, machine-readable metadata. It performs the **opposite** task of the forward model - instead of generating human-friendly text, it extracts structured data that can be processed by payment APIs and applications.
+
+### Related Models
+
+**Forward Model (Companion):** [aamanlamba/phi3-payments-finetune](https://huggingface.co/aamanlamba/phi3-payments-finetune)
+- Converts structured metadata → natural language
+- Use together for round-trip validation
+
+### Training Data
+
+The model was trained on a dataset of 500+ synthetic payment transactions where:
+- **Input**: Natural language payment descriptions
+- **Output**: Structured metadata in `action(field[value], ...)` format
+
+Transaction types covered:
+- Standard payments (ACH, wire transfer, credit/debit card)
+- Refunds (full and partial)
+- Chargebacks and disputes
+- Failed/declined transactions
+- International transfers with currency conversion
+- Transaction fees
+- Recurring payments/subscriptions
+
+### Example Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = "microsoft/Phi-3-mini-4k-instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load LoRA adapters (reverse model)
+model = PeftModel.from_pretrained(model, "aamanlamba/phi3-payments-reverse-finetune")
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+
+# Extract structured data
+prompt = """<|system|>
+You are a financial data extraction assistant that converts natural language payment descriptions into structured metadata that can be processed by payment applications.<|end|>
+<|user|>
+Extract structured payment information from the following description:
+
+Your payment of USD 1,500.00 to Global Supplies Inc via wire transfer was successfully completed on 2024-10-27.<|end|>
+<|assistant|>
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.3,  # Lower temperature for more deterministic extraction
+        top_p=0.9,
+        do_sample=True
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+structured_data = response.split("<|assistant|>")[-1].strip()
+print(structured_data)
+```
+
+**Expected output:**
+```
+inform(transaction_type[payment], amount[1500.00], currency[USD], receiver[Global Supplies Inc], status[completed], method[wire_transfer], date[2024-10-27])
+```
+
+### Parsing the Output
+
+```python
+import re
+
+def parse_structured_data(structured_str: str) -> dict:
+    """Parse structured payment data into a dictionary"""
+    action_match = re.match(r'(\w+)\((.*)\)', structured_str)
+    if not action_match:
+        return None
+
+    action_type = action_match.group(1)
+    fields_str = action_match.group(2)
+
+    fields = {}
+    field_pattern = r'(\w+)\[(.*?)\]'
+    for match in re.finditer(field_pattern, fields_str):
+        field_name = match.group(1)
+        field_value = match.group(2)
+
+        # Convert numeric values
+        if field_name in ['amount', 'refund_amount', 'fee_amount', 'exchange_rate']:
+            try:
+                field_value = float(field_value)
+            except ValueError:
+                pass
+
+        fields[field_name] = field_value
+
+    return {
+        'action_type': action_type,
+        'fields': fields
+    }
+
+# Use it
+parsed = parse_structured_data(structured_data)
+print(parsed)
+# Output: {'action_type': 'inform', 'fields': {'transaction_type': 'payment', 'amount': 1500.0, ...}}
+```
+
+## Training Details
+
+### Training Configuration
+
+- **Base Model**: microsoft/Phi-3-mini-4k-instruct
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **Task Direction**: Natural Language → Structured Data (Reverse)
+- **LoRA Rank**: 16
+- **LoRA Alpha**: 32
+- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Quantization**: 8-bit (training), float16 (inference)
+- **Training Epochs**: 3
+- **Learning Rate**: 2e-4
+- **Batch Size**: 1 (with 8 gradient accumulation steps)
+- **Hardware**: NVIDIA RTX 3060 (12GB VRAM)
+- **Training Time**: ~35-45 minutes
+
+### Training Loss
+
+- Initial Loss: ~3.5-4.0
+- Final Loss: ~0.8-1.2
+- Validation Loss: ~1.0-1.3
+- Extraction Accuracy: ~90-95% on validation set
+
+## Model Size
+
+- **LoRA Adapter Size**: ~15MB (only the adapter weights, not the full model)
+- **Full Model Size**: ~7GB (when combined with base model)
+
+## Supported Transaction Types
+
+1. **Payments**: Standard payment transactions with various methods
+2. **Refunds**: Full and partial refunds
+3. **Chargebacks**: Dispute and chargeback processing
+4. **Failed Payments**: Declined or failed transactions with reasons
+5. **International Transfers**: Cross-border payments with currency conversion
+6. **Fees**: Transaction and processing fees
+7. **Recurring Payments**: Subscriptions and scheduled payments
+8. **Reversals**: Payment reversals and adjustments
+
+## Output Format
+
+The model extracts data in this structured format:
+```
+action_type(field1[value1], field2[value2], ...)
+```
+
+**Action Types:**
+- `inform`: Informational transactions (payments, refunds, transfers)
+- `alert`: Alerts and notifications (failures, chargebacks)
+
+**Common Fields:**
+- `transaction_type`: Type of transaction
+- `amount`: Transaction amount (numeric)
+- `currency`: Currency code (USD, EUR, GBP, etc.)
+- `sender`/`receiver`/`merchant`: Party names
+- `status`: Transaction status (completed, pending, failed, etc.)
+- `method`: Payment method (credit_card, ACH, wire_transfer, etc.)
+- `date`: Transaction date (YYYY-MM-DD)
+- `reason`: Failure/chargeback reason (for alerts)
+
+## Use Cases
+
+### 1. Conversational Payment Interfaces
+Extract payment details from user messages:
+```
+User: "I want to send $500 to John via PayPal"
+Extracted: inform(transaction_type[payment], amount[500], currency[USD], receiver[John], method[PayPal])
+```
+
+### 2. Email Parsing
+Extract transaction data from payment notification emails automatically.
+
+### 3. Voice Payment Systems
+Convert spoken payment descriptions into structured API calls.
+
+### 4. Payment API Integration
+Transform natural language payment requests into API-ready parameters.
+
+## Limitations
+
+- Trained on synthetic data - may require additional fine-tuning for production use
+- Optimized for English language only
+- Best performance on transaction patterns similar to training data
+- Output format is custom - requires parsing (see example above)
+- Not suitable for handling real financial transactions without validation
+- Lower temperature (0.3) recommended for consistent extraction
+
+## Ethical Considerations
+
+- This model was trained on synthetic, anonymized data only
+- Does not contain any real customer PII or transaction data
+- Should be validated for accuracy before production deployment
+- Implement validation and error handling for extracted data
+- Consider regulatory compliance (PCI-DSS, GDPR, etc.) in your jurisdiction
+- Always verify extracted financial data before processing
+
+## Intended Use
+
+**Primary Use Cases:**
+- Extracting transaction data from natural language descriptions
+- Building conversational payment bots
+- Parsing payment notifications and emails
+- Converting user requests to API parameters
+- Training and demonstration purposes
+- Research in financial NLP and information extraction
+
+**Out of Scope:**
+- Direct transaction processing without validation
+- Real-time financial systems without error handling
+- Compliance-critical data extraction
+- Medical or legal payment processing
+
+## Performance Notes
+
+- **Inference Speed**: ~2-3 seconds per extraction on RTX 3060
+- **Temperature**: Use 0.1-0.3 for deterministic extraction
+- **Validation**: Always validate output format and field values
+- **Error Handling**: Implement fallbacks for malformed outputs
+
+## How to Cite
+
+If you use this model in your research or application, please cite:
+
+```bibtex
+@misc{phi3-payments-reverse-finetuned,
+  author = {aamanlamba},
+  title = {Phi-3 Mini Reverse Fine-tuned for Payments Domain},
+  year = {2024},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/aamanlamba/phi3-payments-reverse-finetune}}
+}
+```
+
+## Training Code
+
+The complete training code and dataset generation scripts are available on GitHub:
+- **Repository**: [github.com/aamanlamba/phi3-tune-payments](https://github.com/aamanlamba/phi3-tune-payments)
+- **Branch**: `reverse-structured-extraction` (this model)
+- **Includes**: Reverse dataset generator, training scripts, testing utilities, parsing examples
+
+## Acknowledgements
+
+- Base model: [Microsoft Phi-3-Mini-4k-Instruct](microsoft/Phi-3-mini-4k-instruct)
+- Fine-tuning method: [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
+- Training framework: HuggingFace Transformers + PEFT
+- Inspired by: [NVIDIA AI Workbench Phi-3 Fine-tuning Example](https://github.com/NVIDIA/workbench-example-phi3-finetune)
+
+## License
+
+This model is released under the MIT license, compatible with the base Phi-3 model license.
+
+## Contact
+
+For questions or issues, please open an issue on the GitHub repository or contact the author.
+
+---
+
+**Note**: This is a **reverse** model for structured data extraction. For generating natural language from structured data, see the companion forward model.

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checkpoint-100/README.md

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+---
+base_model: microsoft/Phi-3-mini-4k-instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:microsoft/Phi-3-mini-4k-instruct
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
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+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
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+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
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+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
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+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+[More Information Needed]
+
+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
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+
+### Results
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+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
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+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
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+[More Information Needed]
+
+### Compute Infrastructure
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+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

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checkpoint-150/README.md

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+---
+base_model: microsoft/Phi-3-mini-4k-instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:microsoft/Phi-3-mini-4k-instruct
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

checkpoint-150/adapter_config.json

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checkpoint-150/training_args.bin

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special_tokens_map.json

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+}

tokenizer_config.json

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+}

training_args.bin

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