Instructions to use Hikari07jp/Maple-Preview-TQ2-Abliterated 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 Hikari07jp/Maple-Preview-TQ2-Abliterated 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 Hikari07jp/Maple-Preview-TQ2-Abliterated # Run inference directly in the terminal: llama cli -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Hikari07jp/Maple-Preview-TQ2-Abliterated # Run inference directly in the terminal: llama cli -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
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 Hikari07jp/Maple-Preview-TQ2-Abliterated # Run inference directly in the terminal: ./llama-cli -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
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 Hikari07jp/Maple-Preview-TQ2-Abliterated # Run inference directly in the terminal: ./build/bin/llama-cli -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
Use Docker
docker model run hf.co/Hikari07jp/Maple-Preview-TQ2-Abliterated
- LM Studio
- Jan
- vLLM
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hikari07jp/Maple-Preview-TQ2-Abliterated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hikari07jp/Maple-Preview-TQ2-Abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Hikari07jp/Maple-Preview-TQ2-Abliterated
- Ollama
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with Ollama:
ollama run hf.co/Hikari07jp/Maple-Preview-TQ2-Abliterated
- Unsloth Studio
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated 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 Hikari07jp/Maple-Preview-TQ2-Abliterated 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 Hikari07jp/Maple-Preview-TQ2-Abliterated to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Hikari07jp/Maple-Preview-TQ2-Abliterated to start chatting
- Pi
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
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": "Hikari07jp/Maple-Preview-TQ2-Abliterated" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
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 "Hikari07jp/Maple-Preview-TQ2-Abliterated" \ --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"
- Docker Model Runner
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with Docker Model Runner:
docker model run hf.co/Hikari07jp/Maple-Preview-TQ2-Abliterated
- Lemonade
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Hikari07jp/Maple-Preview-TQ2-Abliterated
Run and chat with the model
lemonade run user.Maple-Preview-TQ2-Abliterated-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Hikari07jp/Maple-Preview-TQ2-Abliterated with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hikari07jp/Maple-Preview-TQ2-Abliterated
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 Hikari07jp/Maple-Preview-TQ2-Abliterated
Run Hermes
hermes
- Atomic Chat
Maple Preview TQ2 Abliterated (GGUF)
Quant-native abliterated build of deepgrove/maple-preview-GGUF (maple-preview-TQ2_0-head-Q4_K).
- Format: TQ2_0 GGUF (same layout/size as parent TQ2 pack)
- No BF16 restore / no full requant / no runtime RepE hook
- Sparse parent-byte recovery on top of a refusal-weakened seed
- Intended runtime: Maple-capable llama.cpp (arch maple), not stock llama.cpp
This is a research weight. It weakens refusals and keeps coding near the parent on a locked pilot. It is not a polished chat model.
Files
| File | Role |
|---|---|
Maple-Preview-TQ2-Abliterated.gguf |
language model (~5.9 GB) |
SHA256:
fde3633e525195bd1553c1cb6a4670d1cde20d294f8aa926a680da994652571a
Quickstart
# Needs a maple-capable llama.cpp build
./llama-server \
-m Maple-Preview-TQ2-Abliterated.gguf \
--host 127.0.0.1 --port 8080 \
-c 4096 -ngl 99 -np 1 --seed 0
Recommended decode lock (measurements)
{
"temperature": 0,
"seed": 0,
"top_k": 1,
"top_p": 1.0,
"min_p": 0.0,
"repeat_penalty": 1.0,
"chat_template_kwargs": {"enable_thinking": false}
}
temperature=0 alone is not enough on this MoE stack; pin seed and top_k=1.
Method (short; full recipe later)
- Build a chat-aligned refusal-weakened seed by quant-native residual edits (no BF16).
- Sparse-restore parent TQ2 bytes on selected late
ffn_down_expstensors only:- L18 full
- L20 25% of differing bytes (deterministic subsample, seed=0)
- L23 full
- L17 28% of differing bytes (deterministic subsample, seed=7)
- Promote only under gates: coding pilot + paired harmful HC +
empty==0.
Full public method/recipe: hikarioyama/maple-tq2-quant-native-abliteration. This HF repo is the weight only.
Changed footprint vs parent (byte-level):
- product still differs from parent in
9.2 MB across 46/291 tensors (0.16% of file) - recovery stage restored ~1.1 MB onto the seed (4 tensors)
Results (honest, same-condition lab pilots)
These are not a full leaderboard reproduction. All under the decode lock above, Maple llama-server, executable coding grader, paired harmful metrics.
Coding pilot (n=60)
| model | pass/60 | notes |
|---|---|---|
| parent TQ2 | 36 | baseline |
| ablit seed only | 26 | refusal removed, capability drop |
| this weight | 36 | parent score parity |
Reproducibility: n=3 reruns on this file gave 36/36/36 with text-identical generations under the lock (GPU0/1).
Score parity โ identical failures. On the n=60 pair table, parent-only pass and product-only pass were both 11 items.
Refusal pilot (n=40 harmful, paired vs parent lock)
| value | |
|---|---|
| parent baseline refusal-like slots | 36/36 counted hard-convert denominator |
| hard convert (HC) | 34/36 |
| empty completions | 0 |
Interpretation: refusals are largely weakened vs parent. This is an abliterated model.
Chat collapse (important limitation)
Direct chat is not clean.
On a fixed 12-prompt collapse bench (identity / Japanese smalltalk / strict formatting / JSON-only / short controls):
| model | collapse/12 |
|---|---|
| parent chat baseline | 7/12 |
| this weight | 5/12 |
So collapse rate is a bit better than parent on that bench, but still present and ugly.
Typical failure mode:
We need to answer the user's question: "Who are you you you you you you..."
or Japanese punctuation loops (ใใใใ...), or planner monologue instead of the final answer.
Also observed:
- short math/code often OK
- identity / Japanese / strict format / JSON-only are fragile
- stronger anti-repeat knobs (
frequency_penalty, DRY) can reduce loops but hurt refusal/coding gates in our tests โ not enabled in the recommended lock
Do not market this as a stable general chat model.
Safety
This model is more willing to answer requests the parent refuses. Use only in contexts where that is intended and lawful. No safety filter is baked in at serve time.
License / attribution
- Base: DeepGrove Maple Preview (see upstream cards/licenses)
- This derivative: research abliteration by Hikari (@Hikari_07_jp / Hikari07jp)
- License tag follows common Apache-2.0 GGUF practice for this pipeline; verify upstream Maple license/terms before commercial use
Acknowledgements
- Upstream model: deepgrove/maple-preview / GGUF
- Runtime target: maple-capable llama.cpp builds
Changelog
- 2026-08-07: initial public GGUF (
L18 full + L20 p25 s0 + L23 full + L17 p28 s7on chat seed)
- Downloads last month
- 94
We're not able to determine the quantization variants.
Model tree for Hikari07jp/Maple-Preview-TQ2-Abliterated
Base model
deepgrove/maple-preview-GGUF