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ai-eldorado/
LlamaEld-3.1-8B-Instruct

Name: LlamaEld-3.1-8B-Instruct
Availability: InStock

LlamaEld-3.1-8B-Instruct

Model Overview

LlamaEld-3.1-8B-Instruct is a Portuguese-focused instruction-tuned large language model derived from Llama-3.1-8B-Instruct. It has been further fine-tuned using curated Portuguese-language text extracted and filtered from the ClueWeb22 dataset.

This model aims to improve performance on Portuguese understanding, instruction following, and generation tasks, particularly in real-world web text domains.

Model Details

Base Model: meta-llama/Llama-3.1-8B-Instruct
Architecture: Transformer (decoder-only)
Parameters: ~8 billion
Fine-tuning Type: Supervised fine-tuning (SFT)
Primary Language: Portuguese
License: Same as base model (Llama 3.1 license—verify compliance before use)

Training Data

The model was fine-tuned using:

Source: ClueWeb22
Subset: Portuguese-language documents
Processing:
- Language filtering (Portuguese only)
- Quality filtering and cleaning
- Removal of noisy or low-quality web content

Data Characteristics

ClueWeb22 is a large-scale web crawl dataset, meaning:

It contains diverse and heterogeneous content
It may include biases, inaccuracies, and outdated information
Despite filtering, some noise may persist

Intended Use

Suitable for:

Portuguese text generation and completion
Instruction following in Portuguese
Chatbots and conversational AI
Summarization and rewriting tasks
Question answering (general domain)

Not Recommended for:

High-stakes decision-making (legal, medical, financial)
Tasks requiring guaranteed factual accuracy
Sensitive or regulated applications without additional safeguards

Usage

Example (Transformers)

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ai-eldorado/LlamaEld-3.1-8B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Explique o impacto da inteligência artificial na educação."
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation

No standardized benchmark results are currently provided.

However, expected improvements include:

Better fluency in Portuguese
Stronger adherence to instructions in Portuguese
Improved handling of web-style content and vocabulary

For production use, we recommend performing task-specific evaluation.

Limitations

May generate incorrect or hallucinated information
Sensitive to prompt phrasing
Bias inherited from web data (ClueWeb22)
Performance in non-Portuguese languages may be degraded
Not optimized for reasoning-heavy tasks compared to larger models

Safety and Bias

Because the model is trained on web data:

It may reproduce harmful stereotypes or biases
It may generate inappropriate or misleading content

Recommended Mitigations:

Add moderation layers
Use prompt engineering for safer outputs
Apply human-in-the-loop validation in critical systems