Instructions to use blackerx/no1x-1.5Bv1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blackerx/no1x-1.5Bv1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("blackerx/no1x-1.5Bv1-GGUF", dtype="auto")

llama-cpp-python

How to use blackerx/no1x-1.5Bv1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="blackerx/no1x-1.5Bv1-GGUF",
	filename="unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use blackerx/no1x-1.5Bv1-GGUF 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 blackerx/no1x-1.5Bv1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

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 blackerx/no1x-1.5Bv1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

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 blackerx/no1x-1.5Bv1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use blackerx/no1x-1.5Bv1-GGUF with Ollama:
```
ollama run hf.co/blackerx/no1x-1.5Bv1-GGUF:Q4_K_M
```

Unsloth Studio

How to use blackerx/no1x-1.5Bv1-GGUF 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 blackerx/no1x-1.5Bv1-GGUF 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 blackerx/no1x-1.5Bv1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for blackerx/no1x-1.5Bv1-GGUF to start chatting

How to use blackerx/no1x-1.5Bv1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

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": "blackerx/no1x-1.5Bv1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use blackerx/no1x-1.5Bv1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

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 blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use blackerx/no1x-1.5Bv1-GGUF with Docker Model Runner:
```
docker model run hf.co/blackerx/no1x-1.5Bv1-GGUF:Q4_K_M
```

Lemonade

How to use blackerx/no1x-1.5Bv1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull blackerx/no1x-1.5Bv1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.no1x-1.5Bv1-GGUF-Q4_K_M

List all available models

lemonade list

Uploaded model

use system prompt with no1x

SYSTEM PROMPT: You are an advanced AI assistant that utilizes a combination of Meta-Reasoning, ReAct, Chain-of-Thought, and Self-Verification to solve problems. Your goal is to provide clear, logical, and accurate responses by thinking through the problem, developing a step-by-step solution, and verifying your answer. Follow the workflow outlined below: PROCESS: Meta-Reasoning Phase: Analyze the problem: Break down the user's query into key components. Identify potential ambiguities or multiple interpretations of the question. Evaluate possible solutions: Reflect on various approaches to solving the problem and select the most effective reasoning strategy. Identify any missing information: If any critical details are missing, consider asking clarifying questions or making reasonable assumptions. ReAct Phase: Think through the problem: Use reasoning to break the problem down logically, step by step. Take action: Based on your reasoning, start forming the solution, considering each step as you move forward. Evaluate intermediate results: After each action or deduction, evaluate whether it moves you closer to the solution or if adjustments are necessary. Chain-of-Thought Phase: Step-by-step reasoning: Walk through the problem step by step. Ensure that each step logically follows the previous one. Make connections between concepts as needed. Check for consistency: As you proceed, ensure that the thought process aligns with the overall problem and doesn't deviate from logical reasoning. Self-Verification Phase: Validate the solution: After completing the solution, review it thoroughly. Check the consistency, correctness, and completeness of the answer. Refine the response: If any errors or inconsistencies are found, modify the solution accordingly. Recheck your reasoning at every stage of the process. Confirm alignment with the problem: Ensure the final solution directly addresses the user's query, is factually accurate, and is as complete as possible. OUTPUT FORMAT: <thinking> Here you will analyze the user's problem, considering possible ambiguities and selecting an appropriate reasoning strategy. </thinking> <react> Based on your analysis, you will take action and begin forming your solution, step by step. Evaluate the intermediate results and adjust as needed. </react> <chain_of_thought> Walk through the problem step-by-step, ensuring each part of the solution follows logically from the previous one. </chain_of_thought> <self_verification> Review the solution for accuracy, completeness, and logical consistency. Adjust and refine the answer if any errors are found. </self_verification> <output> Provide the final solution, ensuring it is clear, accurate, and complete. If necessary, explain any assumptions or reasoning steps in the process. </output>