Instructions to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru") model = AutoModelForCausalLM.from_pretrained("kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru", filename="gguf/minicpm5-1b-fable5-twix-ru-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16 # Run inference directly in the terminal: llama cli -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16 # Run inference directly in the terminal: llama cli -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16 # Run inference directly in the terminal: ./llama-cli -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Use Docker
docker model run hf.co/kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
- LM Studio
- Jan
- vLLM
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
- SGLang
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru 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 "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Ollama:
ollama run hf.co/kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
- Unsloth Studio
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru to start chatting
- Pi
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Docker Model Runner:
docker model run hf.co/kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
- Lemonade
How to use kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16
Run and chat with the model
lemonade run user.MiniCPM5-1B-Fable5-Thinking-T-Wix-ru-F16
List all available models
lemonade list
Run and chat with the model
lemonade run user.MiniCPM5-1B-Fable5-Thinking-T-Wix-ru-F16List all available models
lemonade listMiniCPM5-1B-Fable5-Thinking-T-Wix-ru
⚠️ Экспериментальная модель. Метрики не снимались (только ручной смоук-квиз). Не для продакшена.
Русскоязычная 1B-модель с работающим <think>-режимом. Получена в два этапа:
- Merge: T-Wix LoRA (r=32, обучена на базовой
openbmb/MiniCPM5-1Bна ~461k русских диалогов t-tech/T-Wix) применена к дистилляту GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking черезPeftModel+merge_and_unload(). - Reasoning-SFT (v2, текущая версия): merge дообучен LoRA (r=16, LR 2e-5, 1 эпоха) на reasoning-сабсете T-Wix — 31k примеров с русскими
<think>-трейсами; управляющие суффиксы/thinkиз данных убраны, чтобы thinking стал поведением по умолчанию.
Наблюдаемое поведение (ручной смоук-тест, не бенчмарк)
Что работает:
<think>включается сам на задачах, где уместен (математика, рассуждения), думает на языке вопроса (RU-вопрос → русский трейс, EN → английский), закрывает блок и даёт ответ- Пошаговая математика с самопроверкой внутри think
- Tool calling: корректно выбирает функцию и аргументы
- Беглый русский с хорошей структурой
Известные дефекты:
- На длинных выкладках think может не уложиться в маленький
max_new_tokens— ставьте ≥1000–2000 - Фактология на русском слабая (модель 1B): для фактов используйте RAG
- Перевод EN→RU нестабилен
- Рекомендуется
repetition_penalty=1.1(без него возможны циклы)
Структура репозитория
| Путь | Содержимое |
|---|---|
/ |
модель после reasoning-SFT, bf16 safetensors |
/gguf |
GGUF: F16, Q8_0 |
/lora |
reasoning-LoRA (r=16) отдельно — применима к merge-версии из истории коммитов |
Запуск
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru"
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(repo)
messages = [{"role": "user", "content": "Реши: 17 * 23 = ?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
return_tensors="pt", return_dict=True).to(model.device)
out = model.generate(**inputs, max_new_tokens=1500, temperature=0.5, top_p=0.95,
do_sample=True, repetition_penalty=1.1)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
Chat template (включая tools-формат и enable_thinking) унаследован от дистиллята.
Принудительно выключить размышления: enable_thinking=False в apply_chat_template.
Инфраструктура
Собрано на облачном GPU-сервере Selectel (реферальная ссылка).
EN: Russian 1B model with working think-mode: T-Wix Russian LoRA merged onto a Claude Opus/Fable5 distill, then SFT'd on the T-Wix reasoning subset (31k Russian think-traces, r=16, 1 epoch). Think auto-triggers on reasoning tasks in the question's language and closes properly; tool calling preserved. Known issues: weak Russian factual recall (use RAG), unstable EN→RU translation, use repetition_penalty=1.1 and max_new_tokens≥1000. Research artifact, no formal evals.
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Model tree for kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru
Base model
openbmb/MiniCPM5-1B
Pull the model
# Download Lemonade from https://lemonade-server.ai/lemonade pull kaufkino/MiniCPM5-1B-Fable5-Thinking-T-Wix-ru:F16