license, language, base_model, tags, pipeline_tag, library_name

license	language	base_model	tags	pipeline_tag	library_name
apache-2.0	en	LiquidAI/LFM2.5-1.2B-Base	finance sales-analysis structured-data unsloth lora lfm data-analysis time-series business-intelligence fast-inference efficient-finetuning	text-generation	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
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

# 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,
)