Instructions to use debugdll/Blind-Text-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

debugdll commited on 14 days ago

Commit

f5b664d

verified ·

1 Parent(s): 2ad4a1a

Upload README.md with huggingface_hub

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README.md CHANGED Viewed

@@ -4,12 +4,35 @@ language:
   - ru
   - en
 tags:
-  - llama.cpp
   - gguf
   - moe
   - conversational
   - russian
 pipeline_tag: text-generation
 ---
 # Blind Text Models
@@ -42,6 +65,10 @@ Only one model is in the collection for now. New versions will be added to this
 > The model introduces itself as **Blind 1** — that is how it presents itself when asked. This is a build feature.
 ## Benchmarks
 Instrumental metrics (MMLU and similar) are still being measured and will be added here. Generation speed is already benchmarked:

   - ru
   - en
 tags:
+  - text-generation
   - gguf
+  - llama.cpp
+  - llama-cpp-python
+  - ollama
   - moe
+  - 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
+  - transformers
 pipeline_tag: text-generation
+library_name: llama.cpp
 ---
 # Blind Text Models
 > 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: