Instructions to use AxionLab-official/MiniBot-0.9M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxionLab-official/MiniBot-0.9M-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AxionLab-official/MiniBot-0.9M-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AxionLab-official/MiniBot-0.9M-Base")
model = AutoModelForCausalLM.from_pretrained("AxionLab-official/MiniBot-0.9M-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AxionLab-official/MiniBot-0.9M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxionLab-official/MiniBot-0.9M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-official/MiniBot-0.9M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AxionLab-official/MiniBot-0.9M-Base

SGLang

How to use AxionLab-official/MiniBot-0.9M-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AxionLab-official/MiniBot-0.9M-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-official/MiniBot-0.9M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AxionLab-official/MiniBot-0.9M-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-official/MiniBot-0.9M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AxionLab-official/MiniBot-0.9M-Base with Docker Model Runner:
```
docker model run hf.co/AxionLab-official/MiniBot-0.9M-Base
```

Fix parameter count: card said ~900K, checkpoint is 985,728 Verified against the safetensors artifact: wte 1375x128 = 176,000 wpe 128x128 = 16,384 4 x (attn.c_attn 128x128 + attn.c_proj 128x128 + mlp.c_fc 128x512 + mlp.c_proj 512x128) = 4 x 49,568 = 198,272 ln_f 128 = 128 ------------------------------------------ total = 985,728 tie_word_embeddings=true, so lm_head adds 0. 985,728 is ~9.5% higher than the ~900K on the card. Updated the badge, tagline, overview, table and disclaimer to ~985K. No change to architecture, weights or any other field.

by Compactbot - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-6

Files changed (1) hide show

README.md +5 -6

README.md CHANGED Viewed

@@ -15,18 +15,18 @@ library_name: transformers
 # 🧠 MiniBot-0.9M-Base
-> **Ultra-lightweight GPT-2 style language model (~900K parameters) specialized in Portuguese conversational text.**
 [![Model](https://img.shields.io/badge/🤗%20Hugging%20Face-MiniBot--0.9M--Base-yellow)](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Base)
 [![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 [![Language](https://img.shields.io/badge/Language-Portuguese-blue)](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Base)
-[![Parameters](https://img.shields.io/badge/Parameters-~900K-orange)](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Base)
 ---
 ## 📌 Overview
-**MiniBot-0.9M-Base** is a tiny decoder-only Transformer (~0.9M 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](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Instruct).
@@ -39,7 +39,7 @@ This is a **base (pretrained) model** — trained purely for next-token predicti
 | 🇧🇷 **Language** | Portuguese (primary) |
 | 🧠 **Architecture** | GPT-2 style (Transformer decoder-only) |
 | 🔤 **Embeddings** | GPT-2 compatible |
-| 📉 **Parameters** | ~900K |
 | ⚙️ **Objective** | Causal Language Modeling (next-token prediction) |
 | 🚫 **Alignment** | None (base model) |
@@ -104,7 +104,6 @@ pip install transformers torch
 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)
@@ -150,7 +149,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## ⚠️ Disclaimer
-- Extremely small model (~900K parameters)
 - Limited world knowledge and weak generalization
 - No safety or alignment measures
 - **Not suitable for production use**

 # 🧠 MiniBot-0.9M-Base
+> **Ultra-lightweight GPT-2 style language model (~985K parameters) specialized in Portuguese conversational text.**
 [![Model](https://img.shields.io/badge/🤗%20Hugging%20Face-MiniBot--0.9M--Base-yellow)](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Base)
 [![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 [![Language](https://img.shields.io/badge/Language-Portuguese-blue)](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Base)
+[![Parameters](https://img.shields.io/badge/Parameters-~985K-orange)](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Base)
 ---
 ## 📌 Overview
+**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](https://huggingface.co/AxionLab-official/MiniBot-0.9M-Instruct).
 | 🇧🇷 **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) |
 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)
 ## ⚠️ Disclaimer
+- Extremely small model (~985K parameters)
 - Limited world knowledge and weak generalization
 - No safety or alignment measures
 - **Not suitable for production use**