Blind-Text-Models

Blind Text Models

A collection of language models from the Blind family. Named for its focus on text tasks — from conversation to content generation.

Current Model (single entry)

Only one model is in the collection for now. New versions will be added to this table.

Architecture

• Base architecture: in-house (Blind series)
• 24 blocks, embedding size 2880
• 32 experts, 4 active per token
• Context up to 128K tokens
• Quantization: MXFP4 (expert weights) + Q8_0 (embeddings/output)
• Format: GGUF, runs via llama.cpp / Ollama / llama-cpp-python

What It Is Good At

• Lively conversation — natural answers, casual and business communication in Russian and English
• Explanations — breaks down complex topics clearly
• Text generation — letters, posts, articles, concise summaries
• Instructions and questions — holds context well, knows how to clarify and answer to the point

The model introduces itself as Blind 1 — that is how it presents itself when asked. This is a build feature.

Hardware / VRAM

Runs fully on GPU in ~11.5 GB — fits comfortably in a 12 GB VRAM card, and easily on 16 GB+. CPU-only inference works too (slower). No external API keys or cloud required — fully local and private.

Benchmarks

Instrumental metrics (MMLU and similar) are still being measured and will be added here. Generation speed is already benchmarked:

Parameter estimates:

• Total parameters: 20 914 757 184 (~20.9B) — counted from the model tensors
• Parameters excluding embeddings and output layer: 19.76B

Usage

# llama.cpp
llama-cli -m blind-1.gguf -p "Hello, who are you?"

# llama-cpp-python
from llama_cpp import Llama
llm = Llama(model_path="blind-1.gguf", n_ctx=8192, n_gpu_layers=-1)
print(llm.create_chat_completion(messages=[{"role": "user", "content": "Who are you?"}]))

License

The model is distributed under the Apache 2.0 license.