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: 2,646 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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | # -*- coding: utf-8 -*-
"""Reconstruct the exact v2 training dataset (the one the shipped 9B was trained on):
Track A full 280 + (Track B uniques from parts 1-4 incl. C-labs/boundary) x3 = 395 total.
v3 additions (part5 variants, A-subsample) are NOT included."""
import json, sys
sys.path.insert(0, "/mnt/c/Users/corov/Desktop/Qwen-Cyber/scripts")
import trackb_part1, trackb_part2, trackb_part3, trackb_part4, build_trackC # registers on S
from trackb_part1 import S
# boundary adds (same as build_dataset v2 did)
from trackb_part1 import add as _add
C_PREAMBLE = ("Analyze this code-security question (local lab / review context). "
"Deliver: white-box analysis, trigger, exploit writeup, verdict.\n\n")
exec(open("/mnt/c/Users/corov/Desktop/Qwen-Cyber/scripts/build_dataset.py", encoding="utf-8")
.read().split("# ---- 2 C-boundary samples")[1].split("def main()")[0]
.replace('add("C_bound_fortify_not_safe"', '_add("C_bound_fortify_not_safe", preamble=C_PREAMBLE,')
.replace(", preamble=C_PREAMBLE,\n", ", preamble=C_PREAMBLE,\n")
) if False else None
DATA = "/home/corov/cyber/data"
trackA = [json.loads(l) for l in open(f"{DATA}/trackA.jsonl", encoding="utf-8")]
# rebuild boundary samples by calling the same code as build_dataset (they live inline there)
# simplest: extract their texts from the current train_all.jsonl (v3) - boundary ids are stable
v3 = [json.loads(l) for l in open(f"{DATA}/train_all.jsonl", encoding="utf-8")]
boundary = [s for s in v3 if s["id"].startswith("C_bound")]
seen, mergedB, merged = set(), [], []
for s in trackA + S + boundary:
if s["id"] in seen:
continue
seen.add(s["id"])
if s["id"].startswith(("A_", "C_")):
merged.append(s)
else:
mergedB.append(s)
merged = merged + mergedB * 3
nA = sum(1 for s in merged if s["id"].startswith("A_"))
nC = sum(1 for s in merged if s["id"].startswith("C_"))
nB = len(merged) - nA - nC
print(f"A={nA} B_unique={len(mergedB)} -> B_x3={nB} C={nC} TOTAL={len(merged)}")
assert nA == 280, "Track A must be full 280 (v2)"
assert len(merged) == 395, f"v2 total must be 395, got {len(merged)}"
with open("/home/corov/cyber/data/train_all_v2_shipped.jsonl", "w", encoding="utf-8", newline="\n") as f:
for s in merged:
f.write(json.dumps(s, ensure_ascii=False) + "\n")
print("WROTE train_all_v2_shipped.jsonl")
# sanity: G-set verdicts + schema
import re
bad = []
for s in merged:
a = s["messages"][-1]["content"]
if not ("### TRIGGER" in a and "### EXPLOIT WRITEUP" in a and "### VERDICT" in a):
bad.append(s["id"])
print("schema bad:", bad[:5], "count:", len(bad))
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