TraceAlchemy-Gemma-4-E4B-Finance-IT is a finance-focused instruction-tuned Gemma 4 E4B model trained to improve careful financial reasoning, financial table understanding, unit and scale handling, sign and direction checks, and final-answer consistency.
This repository contains the merged GGUF versions of the fine-tuned model for use with llama.cpp, LM Studio, Ollama, and other GGUF-compatible runtimes.
The model was fine-tuned, merged, and converted to GGUF using Unsloth.
Model Summary
This model was trained as a finance reasoning assistant with a focus on:
Financial statement reasoning
Revenue, margin, growth, and ratio calculations
SEC-style table and excerpt extraction
Unit and scale conversion, such as thousands to millions
Sign and direction reasoning
Multi-step table reasoning
Final-answer consistency checking
General finance instruction following
The goal of this run was not to teach the model static finance facts. Instead, the goal was to improve the model’s behavior on finance reasoning workflows. The training data emphasizes showing calculations, checking units, avoiding unsupported assumptions, and producing clear final answers.
Base Model
Base model:unsloth/gemma-4-E4B-it
Fine-tuned model name:TraceAlchemy-Gemma-4-E4B-Finance-IT
Architecture class: Gemma 4 E4B instruction model
Training method: LoRA supervised fine-tuning
Final format in this repository: GGUF
Gemma 4 E4B is treated as an effective E4B-class model. During training, the loaded parameter count was approximately 8.1B parameters including embeddings.
Training Configuration
Setting
Value
Base model
unsloth/gemma-4-E4B-it
Training method
LoRA SFT
Base loading during training
8-bit
Max sequence length
16,384
LoRA rank
64
LoRA alpha
64
Learning rate
2e-5
Epochs
1
Per-device batch size
2
Gradient accumulation steps
8
Effective batch size
16
Optimizer
adamw_8bit
Training examples
10,000
Evaluation examples
630
Total training steps
625
Training runtime
~2.93 hours on A100-class GPU
Training Completion
The run completed successfully with the following final training output:
The final run completed all 625 planned training steps.
Validation and Evaluation During Training
The model was evaluated every 50 training steps on a held-out evaluation set of 630 examples.
Validation loss is reported as language-modeling loss on the held-out evaluation split. It is not the same thing as benchmark accuracy, but it is useful for checking whether the model is improving on examples it is not directly training on.
The validation trend was strong during the run:
Step
Validation Loss
50
0.765309
100
0.409274
150
0.318539
200
0.286068
250
0.271857
300
0.263259
350
0.257434
The validation loss dropped from 0.765309 at step 50 to 0.257434 by step 350, showing steady improvement on held-out finance examples during training.
This suggests the model was not only fitting the training examples, but also improving on the validation set.
More benchmarks soon.
Dataset Mix
The training set used a 10,000-example finance-focused mixture.
The data recipe was built around a real finance-data anchor from previous experimentation, then expanded with targeted synthetic examples designed to address specific finance reasoning failure modes.
Real Finance Data
Source
Train
Eval
FinanceBench
120
30
TAT-QA
2,200
100
ConvFinQA
1,800
100
FinanceReasoning
750
100
Finance-Instruct-500k
400
50
Total real examples
5,270
380
Synthetic Finance Data
The synthetic examples targeted specific reasoning skills:
Synthetic Category
Train
Eval
Hard SEC-style extraction
1,500
50
Final-answer consistency checks
1,200
50
Sign, scale, and direction reasoning
900
50
Multi-step distractor tables
800
50
Hard unit-scale conversion
330
50
Total synthetic examples
4,730
250
Final Dataset Size
Split
Examples
Train
10,000
Eval
630
Dataset Credits
This model was trained using examples derived from or inspired by the following datasets:
Additional synthetic examples were generated for targeted finance reasoning skills, including SEC-style extraction, scale and unit conversion, sign and direction reasoning, multi-step table reasoning, and final-answer consistency checks.
What Was Tested
During training, the model was tested through validation loss on a held-out evaluation split containing both real and synthetic finance examples.
The evaluation set included:
FinanceBench-style question answering
TAT-QA table and text reasoning
ConvFinQA conversational finance QA
FinanceReasoning-style calculation and reasoning examples
Finance-Instruct examples
Synthetic SEC-style extraction checks
Synthetic unit and scale conversion checks
Synthetic sign and direction checks
Synthetic final-answer consistency checks
Synthetic multi-step distractor table checks
This validation setup was intended to check whether the model could generalize beyond the exact training examples while staying focused on finance reasoning behavior.
Further external benchmark testing is planned.
More benchmarks soon.
Available GGUF Files
File
Notes
gemma-4-E4B-it.Q4_K_M.gguf
Smaller balanced quant
gemma-4-E4B-it.Q5_K_M.gguf
Recommended balanced quality/size option
gemma-4-E4B-it.Q6_K.gguf
Higher quality quant
gemma-4-E4B-it.Q8_0.gguf
Largest quant, closest to higher precision
gemma-4-E4B-it.BF16-mmproj.gguf
Multimodal projector file for multimodal usage
For text-only use, you usually only need one of the main .gguf files, such as Q5_K_M, Q6_K, or Q8_0.
The BF16-mmproj.gguf file is for multimodal usage. It is not required for normal text-only inference.
Recommended GGUF Choice
Quant
Recommendation
Q5_K_M
Good first choice for quality/size balance
Q6_K
Better quality if you have enough VRAM/RAM
Q8_0
Highest quality among the listed quants, but larger
This repository includes a Modelfile generated by Unsloth for Ollama users.
The Modelfile is optional. If you are using llama.cpp, LM Studio, or another runtime that directly loads .gguf files, you can ignore it and simply download the GGUF file you want.
This model is intended for finance reasoning and educational/research workflows, especially tasks involving:
Financial statement reasoning
SEC-style table and excerpt extraction
Revenue, margin, growth, and ratio calculations
Unit and scale conversion
Sign and direction checks
Final-answer consistency checking
General finance instruction following
Limitations
This model should not be treated as a source of guaranteed financial truth.
It may still:
Make calculation mistakes
Misread financial tables
Mis-handle units or scales
Produce unsupported conclusions
Hallucinate details not present in the prompt
Fail on complex accounting, legal, or investment questions
For real investment, accounting, legal, or business decisions, verify outputs against original filings, audited statements, and qualified professionals.
Related Artifacts
This GGUF repository is part of the TraceAlchemy Gemma finance run.
