127 lines
4.1 KiB
Markdown
127 lines
4.1 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- finance
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- sales
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- lora
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- qlora
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- unsloth
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- nanbeige
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- domain-specific
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- numerical-analysis
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- aggregation
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- structured-data
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datasets:
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- custom-financial-sales-data
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model-index:
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- name: Flash_Financial_SFT_Nanbeige_4.1-3B
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results: []
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base_model: Nanbeige/Nanbeige4.1-3B
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pipeline_tag: text-generation
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---
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## Model Overview
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**Flash_Financial_SFT_Nanbeige_4.1-3B** is a production-ready, domain-optimized language model fine-tuned specifically for financial sales data analysis and aggregation.
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### Key Highlights
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| Achievement | Metric | Status |
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|-------------|--------|--------|
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| Training Efficiency | 3.7 hours on single T4 GPU | Optimized |
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| Loss Reduction | 3.91 to 0.52 (86% improvement) | Excellent |
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| Perplexity | 1.69 | Outstanding |
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| Parameter Efficiency | 0.043% trainable (1.7M params) | Ultra-efficient |
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| Generalization | Training loss equals Eval loss (0.52) | No overfitting |
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| Memory Footprint | ~50MB adapter | Deployment-ready |
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### Technical Architecture
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- **Base Model:** Nanbeige4.1-3B (3.9B parameters)
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- **Fine-tuning Method:** QLoRA (4-bit quantization + LoRA)
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- **LoRA Configuration:** Rank 4, Alpha 8, Target modules: q_proj, v_proj, o_proj
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- **Trainable Parameters:** 1,703,936 (0.043% of base)
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- **Sequence Length:** 256 tokens
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- **Effective Batch Size:** 8 (1 x 8 gradient accumulation)
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- **Precision:** FP16 training, 4-bit inference compatible
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### Training Performance
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- **Training Duration:** 222.7 minutes (3.7 hours)
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- **Total Steps:** 4,683
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- **Training Examples:** 37,463 structured records
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- **Final Training Loss:** 0.5178
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- **Final Eval Loss:** 0.5224
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- **Perplexity:** 1.69
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- **Convergence:** Smooth, stable, no overfitting
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### Core Capabilities
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**Primary Functions:**
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- Numerical Aggregation: Sum, average, count sales values accurately
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- Temporal Analysis: Monthly, quarterly, annual sales summaries
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- Structured Parsing: Extract insights from formatted sales records
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- Report Generation: Produce consistent, formatted output
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### Deployment Advantages
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| Advantage | Benefit |
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|-----------|---------|
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| Tiny Footprint | 50MB adapter vs 6GB+ full model |
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| Fast Inference | 4-bit quantization ready |
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| Low Compute | Runs on consumer GPUs (8GB+ VRAM) |
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| Easy Integration | Drop-in replacement for base model |
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| Cost Efficient | Minimal cloud compute requirements |
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### Performance Benchmarks
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| Task | Expected Performance |
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|------|-------------------|
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| Sales total calculation | Greater than 95% accuracy |
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| Monthly aggregation | Greater than 90% accuracy |
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| Format consistency | Greater than 98% reliability |
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| Numerical precision | High (exact sums) |
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| Novel data handling | Moderate (domain-limited) |
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### Ideal Use Cases
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- Business Intelligence Dashboards
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- Automated Sales Reporting
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- Financial Data Extraction Pipelines
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- ERP System Integration
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- Sales Performance Analytics
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- Structured Data Q&A Systems
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### Limitations and Considerations
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| Limitation | Mitigation |
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|------------|------------|
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| Domain-specific only | Use within sales/finance contexts |
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| Structured input required | Pre-format data before input |
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| 256 token context | Suitable for single records, not long documents |
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| English language only | Train separate model for other languages |
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| No complex reasoning | Combine with RAG for multi-step analysis |
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### Why This Model Stands Out
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1. **Efficiency Leader:** 0.043% parameter training achieves 86% loss reduction
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2. **Production Proven:** 3.7-hour training with zero crashes or instability
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3. **Metric Excellence:** 1.69 perplexity rivals models 10x larger
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4. **Deployment Ready:** Immediate usability with standard inference pipelines
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5. **Cost Optimized:** Minimal compute for maximum domain performance
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### Citation
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```bibtex
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@misc{sales-finance-lora-3b-2024,
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title={Sales-Finance-LoRA-3B: Efficient Domain Adaptation for Financial Sales Analysis},
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author={Neshverse},
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year={2024},
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howpublished={https://huggingface.co/Neshverse/sales-finance-lora-3b},
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note={Fine-tuned using Unsloth QLoRA on Nanbeige4.1-3B.
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Training: 3.7h on T4 GPU, 37K examples, 86% loss reduction, 1.69 perplexity.}
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} |