Text Generation
GGUF
llama-cpp-python
Transformers
Russian
English
llama.cpp
ollama
Mixture of Experts
mixture-of-experts
conversational
chat
assistant
instruction-following
large-language-model
llm
quantized
mxfp4
q8_0
4bit
multimodal-text
multilingual
russian
english
local
offline
free
inference
deployment
Instructions to use debugdll/Blind-Text-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use debugdll/Blind-Text-Models with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="debugdll/Blind-Text-Models", filename="blind-1.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Transformers
How to use debugdll/Blind-Text-Models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="debugdll/Blind-Text-Models") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("debugdll/Blind-Text-Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use debugdll/Blind-Text-Models 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 debugdll/Blind-Text-Models # Run inference directly in the terminal: llama cli -hf debugdll/Blind-Text-Models
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf debugdll/Blind-Text-Models # Run inference directly in the terminal: llama cli -hf debugdll/Blind-Text-Models
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 debugdll/Blind-Text-Models # Run inference directly in the terminal: ./llama-cli -hf debugdll/Blind-Text-Models
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 debugdll/Blind-Text-Models # Run inference directly in the terminal: ./build/bin/llama-cli -hf debugdll/Blind-Text-Models
Use Docker
docker model run hf.co/debugdll/Blind-Text-Models
- LM Studio
- Jan
- vLLM
How to use debugdll/Blind-Text-Models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "debugdll/Blind-Text-Models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "debugdll/Blind-Text-Models", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/debugdll/Blind-Text-Models
- SGLang
How to use debugdll/Blind-Text-Models 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 "debugdll/Blind-Text-Models" \ --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": "debugdll/Blind-Text-Models", "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 "debugdll/Blind-Text-Models" \ --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": "debugdll/Blind-Text-Models", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use debugdll/Blind-Text-Models with Ollama:
ollama run hf.co/debugdll/Blind-Text-Models
- Unsloth Desktop
- Pi
How to use debugdll/Blind-Text-Models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf debugdll/Blind-Text-Models
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "debugdll/Blind-Text-Models" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use debugdll/Blind-Text-Models with Docker Model Runner:
docker model run hf.co/debugdll/Blind-Text-Models
- Lemonade
How to use debugdll/Blind-Text-Models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull debugdll/Blind-Text-Models
Run and chat with the model
lemonade run user.Blind-Text-Models-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use debugdll/Blind-Text-Models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf debugdll/Blind-Text-Models
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 debugdll/Blind-Text-Models
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use debugdll/Blind-Text-Models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf debugdll/Blind-Text-Models
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 "debugdll/Blind-Text-Models" \ --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"
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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base_model: openai/gpt-oss-20b
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language:
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- ru
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- en
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tags:
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- llama.cpp
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- gguf
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- moe
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- conversational
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- russian
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pipeline_tag: text-generation
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---
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# Blind Text Models
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Коллекция языковых моделей семейства **Blind**. Назван так за фокус на текстовых задачах — от разговора до генерации контента.
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## Где модель? (пока одна)
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| Модель | Параметры | Квантование | Размер файла | Контекст | Направление |
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|---|---|---|---|---|---|
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| **Blind Text 1** (`blind-1.gguf`) | **20.9B** (MoE, 32 эксперта, 4 активных) | MXFP4 + Q8_0 | 11.3 GB | 131 072 (128K) | Универсальный ассистент, силён в общении, объяснениях и генерации на русском и английском |
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Сейчас в коллекции одна модель. Новые версии будут добавляться в эту же таблицу.
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## Архитектура
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- Базовая архитектура: **gpt-oss-20b** (MoE)
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- 24 блока, размер эмбеддингов 2880
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- 32 эксперта, 4 активных на токен
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- Контекст до **128K токенов**
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- Квантование: MXFP4 (веса экспертов) + Q8_0 (эмбеддинги/выход)
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- Формат: GGUF, запускается через llama.cpp / Ollama / llama-cpp-python
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## На что она заточена
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- **Живое общение** — естественные ответы, разговорное и деловое общение на русском и английском
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- **Объяснения** — понятно раскладывает сложные темы
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- **Генерация текста** — письма, посты, статьи, краткие выжимки
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- **Инструкции и вопросы** — уверенно держит контекст, умеет уточнять и отвечать по делу
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> Модель позиционирует себя как **Blind 1** — так она представляется при знакомстве. Это особенность сборки.
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## Бенчмарки
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Инструментальные метрики (MMLU и подобные) — в процессе измерения, будут добавлены сюда. Скорость работы уже замерена:
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| Окружение | Генерация |
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|---|---|
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| NVIDIA RTX 5080 (16GB), llama.cpp | ≈ **10 ток/с** (100 токенов за ~10 c, с учётом reasoning-префикса) |
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Оценка параметров:
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- Общее число параметров: **20 914 757 184 (~20.9B)** — посчитано по тензорам модели
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- Параметры без эмбеддингов и выходного слоя: **19.76B**
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## Запуск
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```bash
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# llama.cpp
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llama-cli -m blind-1.gguf -p "Привет, кто ты?"
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```
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```python
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# llama-cpp-python
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from llama_cpp import Llama
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llm = Llama(model_path="blind-1.gguf", n_ctx=8192, n_gpu_layers=-1)
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print(llm.create_chat_completion(messages=[{"role": "user", "content": "Кто ты?"}]))
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```
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## Лицензия
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Оригинальные веса gpt-oss распространяются по лицензии Apache 2.0. Сборка и квантование — в том же духе.
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