Instructions to use HBB-Community/HamBoBoGPT-1-Base_mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HBB-Community/HamBoBoGPT-1-Base_mini with Transformers:

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

pipe = pipeline("text-generation", model="HBB-Community/HamBoBoGPT-1-Base_mini")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HBB-Community/HamBoBoGPT-1-Base_mini")
model = AutoModelForCausalLM.from_pretrained("HBB-Community/HamBoBoGPT-1-Base_mini")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HBB-Community/HamBoBoGPT-1-Base_mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HBB-Community/HamBoBoGPT-1-Base_mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HBB-Community/HamBoBoGPT-1-Base_mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HBB-Community/HamBoBoGPT-1-Base_mini

SGLang

How to use HBB-Community/HamBoBoGPT-1-Base_mini 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 "HBB-Community/HamBoBoGPT-1-Base_mini" \
    --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": "HBB-Community/HamBoBoGPT-1-Base_mini",
		"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 "HBB-Community/HamBoBoGPT-1-Base_mini" \
        --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": "HBB-Community/HamBoBoGPT-1-Base_mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HBB-Community/HamBoBoGPT-1-Base_mini with Docker Model Runner:
```
docker model run hf.co/HBB-Community/HamBoBoGPT-1-Base_mini
```

HamBoBoGPT-Reasoning

Маленькая модель на английском языке обученая на более 500 милионов токенов информации

Плюсы👍

Модель очень компактная и может запуститься даже на телефоне
Модель умеет думать
низкий loss

Минусы🥀🥀🥀

Тупая как пень
Может уйти в цикл
Не умеет придумывать адекватные истории

Использованные датасеты

SupraLabs/SupraThink-Dataset-500x
TechAI/claude-haiku-4.5-high-reasoning-1700x
ansulev/GPT-5.5-Thinking-Max-Distill-25k
Jackrong/gpt-oss-120B-distilled-reasoning
andy279/nemotron-reasoning-challenge Кажется мне от них приедет по шапке за использование датасета
Quardo/gsm8k-thinking
Mahfug/claude-opus-4.6-4.7-reasoning-8.7k
alibayram/gemini-3.1-prohard-high-reasoning-1700x
dolbi14/medical-o1-reasoning-SFT
Rombo-Org/Optimized_Reasoning
KingNish/reasoning-base-20k
Gryphe/Opus-4.6-Reasoning-24k
Я же всех указал, да?

📊 Benchmark Results (Baseline: Supra-50M-Reasoning)

Benchmark	Supra-50M-Reasoning	HamBoBoGPT (Ours)
PIQA	59.47%	59.58%
WinoGrande	51.07%	50.75%

Да это же SupraThink_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss_plus-Claude-Haiku-4.5_GPT-5.5-Thinking-Max_Nemotron_GSM8K_Medical-O1_Optimized_Reasoning_v1

Дружеское напоминание, модель стала хуже.