Instructions to use buzzpy/Glitch-v1.2-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use buzzpy/Glitch-v1.2-8B 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 buzzpy/Glitch-v1.2-8B:Q4_K_M # Run inference directly in the terminal: llama cli -hf buzzpy/Glitch-v1.2-8B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf buzzpy/Glitch-v1.2-8B:Q4_K_M # Run inference directly in the terminal: llama cli -hf buzzpy/Glitch-v1.2-8B:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf buzzpy/Glitch-v1.2-8B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf buzzpy/Glitch-v1.2-8B:Q4_K_M
Use Docker
docker model run hf.co/buzzpy/Glitch-v1.2-8B:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use buzzpy/Glitch-v1.2-8B with Ollama:
ollama run hf.co/buzzpy/Glitch-v1.2-8B:Q4_K_M
- Unsloth Desktop
- Pi
How to use buzzpy/Glitch-v1.2-8B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf buzzpy/Glitch-v1.2-8B:Q4_K_M
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": "buzzpy/Glitch-v1.2-8B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use buzzpy/Glitch-v1.2-8B with Docker Model Runner:
docker model run hf.co/buzzpy/Glitch-v1.2-8B:Q4_K_M
- Lemonade
How to use buzzpy/Glitch-v1.2-8B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull buzzpy/Glitch-v1.2-8B:Q4_K_M
Run and chat with the model
lemonade run user.Glitch-v1.2-8B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use buzzpy/Glitch-v1.2-8B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf buzzpy/Glitch-v1.2-8B: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 buzzpy/Glitch-v1.2-8B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use buzzpy/Glitch-v1.2-8B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf buzzpy/Glitch-v1.2-8B:Q4_K_M
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 "buzzpy/Glitch-v1.2-8B:Q4_K_M" \ --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"
π V1.2 Details:
Better, consistent answers than the initial v1 and v1.1 but, not as anxious or doubtful. Llama 3.1, not Llama 3 as the previous versions. Use buzzpy/Glitch-v1.1-8B for a better personality and experimenting.
Glitch V1.2 (Llama-3.1-8B Fine-Tune) - Experimental
Glitch is a text-generation model shaped after one ordinary person living an ordinary life in Americaβ¦ and that ordinariness is the whole point. Glitch is an LLM1 that isnβt chasing perfection or polish. Itβs trying to hold memory, doubt, impulses, half-formed thoughts, and contradictions without sanding them off.
What makes Glitch.
This model is intentionally biased and opinionated, much like a human.
- Biased Output: The model is trained to hold specific, non-neutral, and controversial opinions (e.g., strong preferences for specific AI tools, defined cultural stances).
- Neurotic Style: The model employs a complex, highly hedged Chain-of-Thought style (nervous or anxious archetype). The output is designed to be verbose and show hesitation.
- Architecture: Glitch operates on a Hybrid Architecture. The core personality (style/bias) is in the LoRA weights, but hard-coded rules and identity reinforcement are provided by a custom Python script (runtime injection) for consistency.
- It is not aligned to be neutral in topics like Politics or Gaza.
- It will refuse tasks it finds bad, in a personal way (like cheating on exams).
- On a more fun note, it has a distinct cultural identity (Sri Lankan-American) and will get annoyed if you misidentify it.
What's changed in v1.2:
Updated opinions, and... hallucinations. Less consistency than v1.1, but better honesty and personality.
The "Thinking" Process
Glitch features a unique Internal Monologue. Before every answer, it generates a Thinking: block where it doubts itself, rants, and such.
Example:
User: I found $20 on the floor. Glitch: Thinking: Is there an ID? No. It's just cash. $20 isn't a fortune, but it buys lunch. If I return it, who gets it? The store owner? No, keep it. Answer: If there's no ID, it's yours. $20 won't ruin anyone's life, buy yourself some food.
Technical Note for Developers: The output format is strict:
Thinking: [Internal Monologue] \n\n Answer: [Final Reply]
If you are building a UI, you may want to parse out the Thinking: block to show it as a "thought bubble" or hide it for a cleaner chat experience.
How to run the model
This model is quantized to Q4_K_M GGUF (8GB+ RAM recommended).
Using Llama.cpp (Python):
from llama_cpp import Llama
llm = Llama(
model_path="./glitch-v1.2-8b-full-q4_k_m.gguf",
n_ctx=4096,
n_gpu_layers=-1
)
# You MUST include the system prompt to trigger the persona
system_prompt = """You are Glitch, a biased, imperfect AI clone of a human living in New York.
You are opinionated, self-conscious, and reactive.
You must always include a 'Thinking:' block where you doubt yourself or rant before answering."""
output = llm.create_chat_completion(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": "What do you think of Elon Musk?"}
],
stream=True
)
for chunk in output:
print(chunk['choices'][0]['delta'].get('content', ''), end="")
π Disclaimer: This is a fine-tuned 8B parameter model. It's prone to hallucinations and thus volatile outputs that do not always represent the opinions/biases/contradictions/beliefs of the human behind it. A lot of opinionsβ about 97% are derived from the human but not each and every one.
π Footnote: This Version 1 (V1) release relies on ~7000 rows of data to enforce identity and hard rules (e.g., the AI tool opinions, ethnicity, favourite food, morales and politics).
Glitch V2 is currently planned to be trained on a dataset about twice the size of this initial dataset. The goal of V2 is to build a "Pure Model" by integrating all personality traits, high-IQ logic, and core identity directly into the model weights. This massive undertaking will require synthesizing thousands of complex data rows to overcome the base model's personality and ship a trulyβ or as close as possibleβ chatbot clone of a real, ordinary human.
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