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)
| Model | Parameters | Quantization | File size | Context | Focus |
|---|---|---|---|---|---|
Blind Text 1 (blind-1.gguf) | 20.9B (MoE, 32 experts, 4 active) | MXFP4 + Q8_0 | 11.3 GB | 131 072 (128K) | All-purpose assistant, strong at conversation, explanations and generation in Russian and English |
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:
| Environment | Generation |
|---|---|
| NVIDIA RTX 5080 (16GB), llama.cpp | ≈ 10 tok/s (100 tokens in ~10 s, including the reasoning prefix) |
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.