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AxionLab-official
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AxionLab-official
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MiniBot-0.9M-Base is a tiny decoder-only Transformer (~985K parameters) based on the GPT-2 architecture, designed for efficient text generation in Portuguese.
This is a base (pretrained) model — trained purely for next-token prediction, with no instruction tuning or alignment of any kind. It serves as the foundation for fine-tuned variants such as MiniBot-0.9M-Instruct.
| Attribute | Detail |
|---|---|
| 🇧🇷 Language | Portuguese (primary) |
| 🧠 Architecture | GPT-2 style (Transformer decoder-only) |
| 🔤 Embeddings | GPT-2 compatible |
| 📉 Parameters | ~985K (985,728) |
| ⚙️ Objective | Causal Language Modeling (next-token prediction) |
| 🚫 Alignment | None (base model) |
MiniBot-0.9M follows a scaled-down GPT-2 design:
Despite its small size, it preserves the core inductive biases of GPT-2, making it well-suited for experimentation and educational purposes.
The model was trained on a Portuguese conversational dataset focused on language pattern learning.
Training notes:
⚠️ This model behaves as a statistical language generator, not a reasoning system.
bash
pip install transformers torchpython
from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "AxionLab-official/MiniBot-0.9M-Base"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name) prompt = "User: Me explique o que é gravidade\nBot:"inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=50, temperature=0.8, top_p=0.95, do_sample=True,) print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Parameter | Recommended Value | Description |
|---|---|---|
temperature | 0.7 – 1.0 | Controls randomness |
top_p | 0.9 – 0.95 | Nucleus sampling |
do_sample | True | Enable sampling |
max_new_tokens | 30 – 80 | Response length |
💡 Base models generally benefit from higher temperature values compared to instruct variants, since there is no fine-tuning to constrain the output distribution.
| Use Case | Suitability |
|---|---|
| 🧠 Fine-tuning (chat, instruction, roleplay) | ✅ Ideal |
| 🎮 Prompt playground & experimentation | ✅ Ideal |
| 🔬 Research on tiny LLMs | ✅ Ideal |
| 📉 Benchmarking small architectures | ✅ Ideal |
| ⚡ Local / CPU-only applications | ✅ Ideal |
| 🏭 Critical production environments | ❌ Not recommended |
MiniBot-0.9M-InstructDistributed under the MIT License. See LICENSE for more details.
Developed by AxionLab 🔬
from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "AxionLab-official/MiniBot-0.9M-Base"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name) prompt = "User: Me explique o que é gravidade\nBot:"inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=50, temperature=0.8, top_p=0.95, do_sample=True,) print(tokenizer.decode(outputs[0], skip_special_tokens=True))