Instructions to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent: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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent: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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Use Docker
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Ollama:
ollama run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- Unsloth Studio
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
- Pi
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent: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": "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Docker Model Runner:
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- Lemonade
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Run and chat with the model
lemonade run user.qwen3.8-9b-cyber-exploit-agent-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent: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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent: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 "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent: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"
File size: 1,457 Bytes
778e97e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import numpy as np
from gguf import GGUFReader, GGUFWriter
SRC = "/home/corov/cyber/gguf/qwen38-cyber-f16.gguf"
DST = "/home/corov/cyber/gguf/qwen38-cyber-f16-patched.gguf"
r = GGUFReader(SRC)
def scalar(f):
vt = f.types[0] # GGUFMetadataType ordinal
arr = f.parts[f.data[0]]
if vt == 8: # STRING
return arr.tobytes().decode("utf-8", "replace")
if vt == 7: # BOOL (stored as uint8)
return bool(arr[0])
if vt in (6, 11): # FLOAT32/64
return float(arr[0])
return int(arr[0])
arch = None
patched = []
for k, f in r.fields.items():
if f.types[0] == 12 or len(f.data) > 1: # ARRAY: types[0]=12
# arrays: parts[data[0]] holds the raw data; parts[data[1]] holds lengths? in gguf-py arrays are
# stored as f.data list of (part_idx) for data; replicate via add_array with the numpy array
arr = f.parts[f.data[0]]
# element type ordinal kept in f.types[1]
if k == "tokenizer.chat_template" or "tokens" in k or "scores" in k or "merges" in k or "type" in k:
continue_compat = True
yield_later = (k, arr)
ARRAYS.append((k, arr))
continue
v = scalar(f)
if k == "general.architecture":
arch = v
continue
if "nextn_predict_layers" in k:
print(f"patch {k}: {v} -> 0")
v = 0
patched.append(k)
SCALARS.append((k, v))
ARRAYS, SCALARS = [], []
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