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Model: NeshVerse/Flash-financial-analysis-lfm-1.2b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+  - en
+base_model: LiquidAI/LFM2.5-1.2B-Base
+tags:
+  - finance
+  - sales-analysis
+  - structured-data
+  - unsloth
+  - lora
+  - lfm
+  - data-analysis
+  - time-series
+  - business-intelligence
+  - fast-inference
+  - efficient-finetuning
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# ⚡ Flash-Financial-Analysis-LFM-1.2B
+
+**Lightning-fast financial intelligence for structured data analysis**
+
+A blazing-fast, customized, lightweight language model optimized for real-time sales & stock analytics, inventory insights, and financial reporting based on the LiquidAI 1.2B base model supervised fine-tuned FP16 model.
+
+## Rag Model Github Repo:
+https://github.com/neshverse/Flash-RAG-web-GUI/tree/main
+
+## Space (Real-time Testing) 
+https://huggingface.co/spaces/NeshVerse/Flash-financial-analysis-lfm-1.2b
+
+Note: If you get any error while chatting, just refresh the page.
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Architecture** | [LiquidAI/LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) |
+| **Fine-tuning** | LoRA (r=4, alpha=8) |
+| **Context Window** | 1,024 tokens |
+| **Precision** | FP16 |
+| **Parameters** | 1.2B base + ~500K LoRA |
+
+## Training Summary
+
+- **Total Samples**: 39,435 (37,463 train / 1,972 validation)
+- **Training Duration**: 2.4 hours
+- **Final Loss**: 0.497 (train) / 0.508 (validation)
+- **Hardware**: Consumer GPU (T4)
+
+## Capabilities
+
+- **Sales Analytics**: Real-time sales data querying and analysis
+- **Stock Analytics**: Inventory levels, turnover rates, stock movement
+- **Financial Reporting**: Automated report generation from structured data
+- **Inventory Insights**: Product performance, seasonal trends, demand forecasting
+
+## Performance
+
+- **Inference Speed**: ~0.55 it/s (T4 GPU)
+- **Memory Usage**: ~6GB (4-bit loaded)
+- **Batch Size**: 4 (effective 8 with grad accum)
+- **Max Sequence**: 1,024 tokens
+  
+## Limitations
+
+- Optimized for structured financial/sales data queries
+- Context window limited to 1,024 tokens
+- Training data from 2022-2023; may not reflect current market conditions
+- Best performance on English language inputs
+
+## Model Files
+
+| File | Format | Size | Description | Use Case |
+|------|--------|------|-------------|----------|
+| `model.safetensors` | FP16 | ~2.4 GB | Original full precision | Maximum quality, GPU inference |
+| `flash-financial-analysis-q8_0.gguf` | Q8_0 | ~1.2 GB | 8-bit quantized (llama.cpp) | CPU inference, Ollama, LM Studio |
+
+## Quantized Version (Q8_0)
+
+We now provide a **Q8_0 quantized version** for easier deployment:
+
+- **Format**: GGUF (llama.cpp compatible)
+- **Size**: ~50% smaller than FP16 (1.2 GB vs 2.4 GB)
+- **Quality**: ~99.9% of original performance
+- **Tools**: Works with llama.cpp, Ollama, LM Studio, llama-cpp-python
+
+### Download Q8_0
+
+```bash
+# Using huggingface-cli
+huggingface-cli download NeshVerse/Flash-financial-analysis-lfm-1.2b flash-financial-analysis-q8_0.gguf
+
+# Or direct download
+wget https://huggingface.co/NeshVerse/Flash-financial-analysis-lfm-1.2b/resolve/main/flash-financial-analysis-q8_0.gguf
+
+## Quick Start
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "NeshVerse/Flash-financial-analysis-lfm-1.2b",
+    max_seq_length=1024,
+    load_in_4bit=True,
+    trust_remote_code=True,
+)