metadata

library_name: transformers
tags:
  - lora
  - finetuned
  - gemma
  - causal-lm
datasets:
  - HuggingFaceH4/Multilingual-Thinking
base_model:
  - google/gemma-2b
co2_eq_emissions:
  emissions: 10
  source: N/A
  training_type: fine-tuning using LoRA
  geographical_location: EU
  hardware_used: Google GPU T4

MCOLLM-2b

A simple 2b model, fine-tuned version of the Gemma 2b model, optimized for step by step thinking

Model Details

Model Description

Developed by: Martico2432
Model type: Causal Language Model (transformer)
Language(s) (NLP): English
License: Apache-2.0
Finetuned from model: Gemma-2B

Model Sources

Repository: (https://huggingface.co/google/gemma-2b)

Uses

Direct Use

Chatbots and conversational AI applications
Text generation for creative or educational purposes
Experimentation with LoRA fine-tuning on small datasets

Downstream Use

Can be further fine-tuned for any specific tasks

Out-of-Scope Use

Not designed for high-stakes decision making (legal, medical, safety-critical)
May generate biased, offensive, or factually incorrect text
Limited generalization due to small fine-tuning dataset

Bias, Risks, and Limitations

Fine-tuned on a very small dataset (1000 examples) → risk of overfitting or narrow outputs
Model may inherit biases from base Gemma‑2B
Outputs should be critically evaluated before deployment

Recommendations

Monitor outputs for unsafe or biased content
Use in low-stakes research or prototyping environments

How to Get Started with the Model

You can get started by using the example in the files

Training Details

Training Data

1000 examples of thinking
Gemma 2b tokenizer

Training Procedure

Fine-tuning via LoRA on top of Gemma-2B base
3 epochs, small learning rate

Training Hyperparameters

Training regime: fp16 mixed precision

Model Examination [optional]

[More Information Needed]

Environmental Impact

Hardware Type: T4
Hours used: 0.5
Cloud Provider: Google Cloud
Compute Region: EU
Carbon Emitted: 0.01kg