hassanepulga/Mateno-v1.5-2b-it

Model Card for Model ID

Mateno-v1.5-2B-it is the fine-tuned version of Google's Gemma model for more specific tasks such as shorthand (Stenography), law or administration. This model is still in training and a new version of 9b parameters will be available soon. From the Mateno.ai project aimed at helping Malian students in their studies by facilitating access to information more quickly and efficiently. Mateno-v1.5-2B-it is a good start because it already meets several needs and its learning capacity is amazing. In a few months, the model should be able to answer questions relating to these areas in a purely Malian context, ensuring the veracity of the information and its relevance. We plan to later develop a model from scratch to contextualize it more advantageously, but for now, we are focusing on large publicly available language models like Llama from Meta, Gemma from Google, Deepseek-R1 from the Chinese firm deepseek.ai to achieve our goals.

Model Details

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Model Description

Mateno-v1.5-2B-it is a text-generation model for more specific tasks.

• Developed by: Hassane SANOGO
• Funded by : Mateno.ai
• Shared by : Hassane SANOGO
• Model type: LLM
• Language(s) (NLP): [More Information Needed]
• License: MIT
• Finetuned from model : Gemma-2B

Model Sources [optional]

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• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with: pip install -U transformers

Then, copy the snippet from the section that is relevant for your usecase. **Running with the pipeline API

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="hassanepulga/Mateno-v1.5-2B-it",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",  # replace with "mps" to run on a Mac device
)

messages = [
    {"role": "user", "content": "Who are you? Please, answer in pirate-speak."},
]

outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)
# Ahoy, matey! I be Gemma, a digital scallywag, a language-slingin' parrot of the digital seas. I be here to help ye with yer wordy woes, answer yer questions, and spin ye yarns of the digital world.  So, what be yer pleasure, eh? 🦜

This snippet is from origin model Gemma-2-2b-it stored in Google's huggingface page.

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

All question out of stenography, law and administration could be rejected by the model. If answered, it could probably be not accurate or wrong. So pay attention when using this Mateno version or above. It's built for law, stenography and administration technical tasks.

[More Information Needed]

Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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