tmr-ai-nano

Gemma-3-270M - Fine-tuned for Financial Instructions

This is a fine-tuned version of Google's gemma-3-270m-it model, adapted for financial instruction-following tasks.

Model Description

This model was fine-tuned using the Apple MLX framework. The goal was to specialize the base model for financial reporting summary and decision-making assistance. It was trained on the Josephgflowers/Finance-Instruct-500k dataset.

Intended Use

This model is intended for tasks related to the financial domain, such as:

• Answering questions about financial concepts.
• Summarizing financial reports.
• Following instructions based on financial data.

How to Use

You can use this model with the transformers library just like any other standard Hugging Face model.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "tlgoa/tmr-ai-nano"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Note: Gemma 3 uses a specific chat template.
# For single-turn inference, you can format it like this:
prompt = "What is the difference between revenue and profit?"
formatted_prompt = f"### User:\n{prompt}\n\n### Assistant:"

inputs = tokenizer(formatted_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Clean up the response to only show the assistant's part
assistant_response = response.split("### Assistant:")[1].strip()

print(assistant_response)

Training Procedure

Dataset

The model was fine-tuned on the Josephgflowers/Finance-Instruct-500k dataset. The data was preprocessed to fit the following format:

### User:
{user_prompt}

### Assistant:
{assistant_response}

Fine-tuning

The model was fine-tuned directly (full parameter tuning) using an Adam optimizer. Due to challenges with LoRA implementation in the available MLX version, a full fine-tuning approach was chosen. The fine-tuned weights were originally saved in MLX's .npz format and subsequently converted back to Hugging Face safetensors format for distribution.

Licenses

• Base Model: This model is based on Google's Gemma-3-270M, which is subject to the Gemma Terms of Use.
• Dataset: The training data from Josephgflowers/Finance-Instruct-500k is available under the Apache 2.0 License.