Hugging Face's logo

PyThaGo
/
LLMLit

GGUF
English
Romanian
LLMLiT
Romania
LLM
Inference Endpoints
imatrix
conversational
Model card Files Community
4
LLMLit / README.md
Cristian Sas
Update README.md
f505f90 verified
preview code
|
raw
history blame
5.18 kB
metadata
license: mit
language:
  - en
  - ro
base_model:
  - LLMLit/LLMLit
tags:
  - LLMLiT
  - Romania
  - LLM

Model Card for LLMLit

Quick Summary

LLMLit is a high-performance, multilingual large language model (LLM) fine-tuned from Meta's Llama 3.1 8B Instruct model. Designed for both English and Romanian NLP tasks, LLMLit leverages advanced instruction-following capabilities to provide accurate, context-aware, and efficient results across diverse applications.

Model Details

Model Description

LLMLit is tailored to handle a wide array of tasks, including content generation, summarization, question answering, and more, in both English and Romanian. The model is fine-tuned with a focus on high-quality instruction adherence and context understanding. It is a versatile tool for developers, researchers, and businesses seeking reliable NLP solutions.

  • Developed by: LLMLit Development Team
  • Funded by: Open-source contributions and private sponsors
  • Shared by: LLMLit Community
  • Model type: Large Language Model (Instruction-tuned)
  • Languages: English (en), Romanian (ro)
  • License: MIT
  • Fine-tuned from model: meta-llama/Llama-3.1-8B-Instruct

Model Sources

Uses

Direct Use

LLMLit can be directly applied to tasks such as:

  • Generating human-like text responses
  • Translating between English and Romanian
  • Summarizing articles, reports, or documents
  • Answering complex questions with context sensitivity

Downstream Use

When fine-tuned or integrated into larger ecosystems, LLMLit can be utilized for:

  • Chatbots and virtual assistants
  • Educational tools for bilingual environments
  • Legal or medical document analysis
  • E-commerce and customer support automation

Out-of-Scope Use

LLMLit is not suitable for:

  • Malicious or unethical applications, such as spreading misinformation
  • Highly sensitive or critical decision-making without human oversight
  • Tasks requiring real-time, low-latency performance in constrained environments

Bias, Risks, and Limitations

Bias

  • LLMLit inherits biases present in the training data. It may produce outputs that reflect societal or cultural biases.

Risks

  • Misuse of the model could lead to misinformation or harm.
  • Inaccurate responses in complex or domain-specific queries.

Limitations

  • Performance is contingent on the quality of input instructions.
  • Limited understanding of niche or highly technical domains.

Recommendations

  • Always review model outputs for accuracy, especially in sensitive applications.
  • Fine-tune or customize for domain-specific tasks to minimize risks.

How to Get Started with the Model

To use LLMLit, install the required libraries and load the model as follows:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

LLMLit is fine-tuned on a diverse dataset containing bilingual (English and Romanian) content, ensuring both linguistic accuracy and cultural relevance.

Training Procedure

Preprocessing

  • Data was filtered for high-quality, instruction-based examples.
  • Augmentation techniques were used to balance linguistic domains.

Training Hyperparameters

  • Training regime: Mixed precision (fp16)
  • Batch size: 512
  • Epochs: 3
  • Learning rate: 2e-5

Speeds, Sizes, Times

  • Checkpoint size: ~16GB
  • Training time: Approx. 1 week on 8 A100 GPUs

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluation was conducted on multilingual benchmarks, such as:

  • FLORES-101 (Translation accuracy)
  • HELM (Instruction-following capabilities)

Factors

Evaluation considered:

  • Linguistic fluency
  • Instruction adherence
  • Contextual understanding

Metrics

  • BLEU for translation tasks
  • ROUGE-L for summarization
  • Human evaluation scores for instruction tasks

Results

LLMLit achieves state-of-the-art performance on instruction-following tasks for English and Romanian, with BLEU scores surpassing comparable models.

Summary

LLMLit excels in bilingual NLP tasks, offering robust performance across diverse domains while maintaining instruction adherence and linguistic accuracy.

Model Examination

Efforts to interpret the model include:

  • Attention visualization
  • Prompt engineering guides
  • Bias audits

Environmental Impact

Training LLMLit resulted in estimated emissions of ~200 kg CO2eq. Carbon offsets were purchased to mitigate environmental impact. Future optimizations aim to reduce energy consumption.

Civis3.png