Instructions to use HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss") model = AutoModelForMultimodalLM.from_pretrained("HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss 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_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss" # 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_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss
- SGLang
How to use HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss 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_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss" \ --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_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss", "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_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss" \ --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_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss with Docker Model Runner:
docker model run hf.co/HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss
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.
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Model tree for HBB-Community/HamboboGPT-1_based-on-Supra_distilgpt_distil-Opus_high_reasoning-distilOpus_distillgptoss
Base model
SupraLabs/Supra-50M-Base