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llama.cpp
qwen
qwen3
qwen3.5
cybersecurity
malware-analysis
reverse-engineering
pe
elf
ghidra
agent
research
conversational
Instructions to use AgentreBench/xref-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AgentreBench/xref-9b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AgentreBench/xref-9b", filename="xref-9b-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AgentreBench/xref-9b 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 AgentreBench/xref-9b:F16 # Run inference directly in the terminal: llama cli -hf AgentreBench/xref-9b:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AgentreBench/xref-9b:F16 # Run inference directly in the terminal: llama cli -hf AgentreBench/xref-9b:F16
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 AgentreBench/xref-9b:F16 # Run inference directly in the terminal: ./llama-cli -hf AgentreBench/xref-9b:F16
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 AgentreBench/xref-9b:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf AgentreBench/xref-9b:F16
Use Docker
docker model run hf.co/AgentreBench/xref-9b:F16
- LM Studio
- Jan
- vLLM
How to use AgentreBench/xref-9b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AgentreBench/xref-9b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AgentreBench/xref-9b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AgentreBench/xref-9b:F16
- Ollama
How to use AgentreBench/xref-9b with Ollama:
ollama run hf.co/AgentreBench/xref-9b:F16
- Unsloth Studio
How to use AgentreBench/xref-9b 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 AgentreBench/xref-9b 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 AgentreBench/xref-9b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AgentreBench/xref-9b to start chatting
- Pi
How to use AgentreBench/xref-9b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AgentreBench/xref-9b:F16
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": "AgentreBench/xref-9b:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AgentreBench/xref-9b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AgentreBench/xref-9b:F16
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 AgentreBench/xref-9b:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use AgentreBench/xref-9b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AgentreBench/xref-9b:F16
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 "AgentreBench/xref-9b:F16" \ --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 AgentreBench/xref-9b with Docker Model Runner:
docker model run hf.co/AgentreBench/xref-9b:F16
- Lemonade
How to use AgentreBench/xref-9b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AgentreBench/xref-9b:F16
Run and chat with the model
lemonade run user.xref-9b-F16
List all available models
lemonade list
File size: 5,995 Bytes
ae8c826 | 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | // Static Ghidra headless summary script for AgentRE.
// Usage from analyzeHeadless:
// -postScript AgentRESummary.java /path/to/output.txt
import ghidra.app.script.GhidraScript;
import ghidra.program.model.address.Address;
import ghidra.program.model.listing.Data;
import ghidra.program.model.listing.DataIterator;
import ghidra.program.model.listing.Function;
import ghidra.program.model.listing.FunctionIterator;
import ghidra.program.model.listing.Instruction;
import ghidra.program.model.listing.InstructionIterator;
import ghidra.program.model.listing.Listing;
import ghidra.program.model.mem.MemoryBlock;
import ghidra.program.model.symbol.Symbol;
import ghidra.program.model.symbol.SymbolIterator;
import java.io.File;
import java.io.FileWriter;
import java.io.PrintWriter;
public class AgentRESummary extends GhidraScript {
private String clean(String s) {
if (s == null) {
return "";
}
s = s.replace('\n', ' ').replace('\r', ' ');
if (s.length() > 240) {
return s.substring(0, 240) + "...";
}
return s;
}
public void run() throws Exception {
String[] args = getScriptArgs();
if (args.length < 1) {
println("AgentRESummary requires an output path argument");
return;
}
File outFile = new File(args[0]);
try (PrintWriter out = new PrintWriter(new FileWriter(outFile))) {
Listing listing = currentProgram.getListing();
out.println("== Program ==");
out.println("name: " + currentProgram.getName());
out.println("format: " + currentProgram.getExecutableFormat());
out.println("language: " + currentProgram.getLanguageID());
out.println("compiler: " + currentProgram.getCompilerSpec().getCompilerSpecID());
out.println("image_base: " + currentProgram.getImageBase());
out.println();
out.println("== Memory Blocks ==");
for (MemoryBlock block : currentProgram.getMemory().getBlocks()) {
out.println(block.getName()
+ " start=" + block.getStart()
+ " end=" + block.getEnd()
+ " size=" + block.getSize()
+ " r=" + block.isRead()
+ " w=" + block.isWrite()
+ " x=" + block.isExecute());
}
out.println();
out.println("== External Symbols / Imports ==");
int count = 0;
SymbolIterator symbols = currentProgram.getSymbolTable().getExternalSymbols();
while (symbols.hasNext() && count < 600) {
Symbol sym = symbols.next();
out.println(sym.getName(true));
count++;
}
if (count >= 600) {
out.println("[truncated imports at 600]");
}
out.println();
out.println("== Strings ==");
count = 0;
DataIterator data = listing.getDefinedData(true);
while (data.hasNext() && count < 500) {
Data d = data.next();
Object value = null;
try {
value = d.getValue();
} catch (Throwable ignored) {
value = null;
}
if (value instanceof String) {
String text = clean((String) value);
if (text.length() >= 4) {
out.println(d.getAddress() + " " + text);
count++;
}
}
}
if (count >= 500) {
out.println("[truncated strings at 500]");
}
out.println();
out.println("== Functions ==");
count = 0;
FunctionIterator funcs = listing.getFunctions(true);
while (funcs.hasNext() && count < 220) {
Function f = funcs.next();
out.println(f.getEntryPoint() + " " + f.getName(true));
count++;
}
if (count >= 220) {
out.println("[truncated functions at 220]");
}
out.println();
out.println("== Function Instruction Excerpts ==");
int funcsPrinted = 0;
funcs = listing.getFunctions(true);
while (funcs.hasNext() && funcsPrinted < 40) {
Function f = funcs.next();
String name = f.getName(true).toLowerCase();
boolean interesting = name.contains("main")
|| name.contains("connect")
|| name.contains("socket")
|| name.contains("send")
|| name.contains("recv")
|| name.contains("exec")
|| name.contains("crypt")
|| name.contains("decrypt")
|| name.contains("attack")
|| name.contains("scan")
|| name.contains("loader")
|| name.contains("start");
if (!interesting && funcsPrinted >= 12) {
continue;
}
out.println();
out.println("-- " + f.getEntryPoint() + " " + f.getName(true) + " --");
InstructionIterator instrs = listing.getInstructions(f.getBody(), true);
int insnCount = 0;
while (instrs.hasNext() && insnCount < 45) {
Instruction insn = instrs.next();
Address addr = insn.getAddress();
out.println(" " + addr + " " + insn.toString());
insnCount++;
}
funcsPrinted++;
}
}
println("AgentRE summary written to " + outFile.getAbsolutePath());
}
}
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