Instructions to use edoggy/audit-coder-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use edoggy/audit-coder-7b-v1 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 edoggy/audit-coder-7b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf edoggy/audit-coder-7b-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf edoggy/audit-coder-7b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf edoggy/audit-coder-7b-v1: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 edoggy/audit-coder-7b-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf edoggy/audit-coder-7b-v1:Q4_K_M

Use Docker

docker model run hf.co/edoggy/audit-coder-7b-v1:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use edoggy/audit-coder-7b-v1 with Ollama:
```
ollama run hf.co/edoggy/audit-coder-7b-v1:Q4_K_M
```
Unsloth Desktop

How to use edoggy/audit-coder-7b-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf edoggy/audit-coder-7b-v1: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": "edoggy/audit-coder-7b-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use edoggy/audit-coder-7b-v1 with Docker Model Runner:
```
docker model run hf.co/edoggy/audit-coder-7b-v1:Q4_K_M
```

Lemonade

How to use edoggy/audit-coder-7b-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull edoggy/audit-coder-7b-v1:Q4_K_M

Run and chat with the model

lemonade run user.audit-coder-7b-v1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use edoggy/audit-coder-7b-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf edoggy/audit-coder-7b-v1: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 edoggy/audit-coder-7b-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use edoggy/audit-coder-7b-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf edoggy/audit-coder-7b-v1: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 "edoggy/audit-coder-7b-v1: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"

edoggy commited on 22 days ago

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@@ -32,7 +32,7 @@ system prompt the model was trained with; without it you get a generic chat mode
 Then point it at a contract:
 ```bash
-ollama run audit-7b-v1 "$(cat src/Vault.sol)" > hypotheses.json
 ```
 Keep the input under about 12,000 tokens (one or a few related contracts). Larger inputs get
@@ -50,7 +50,7 @@ cut off.
 ## Training
-LoRA on Qwen2.5-Coder-7B-Instruct, trained on High and Medium findings from 125 past
 audits, paired with the code they were found in. The adapter is in `adapter/` if you want to
 merge or train further. Credit to the wardens who found the bugs.

 Then point it at a contract:
 ```bash
+ollama run --nowordwrap audit-7b-v1 "$(cat src/Vault.sol)" > hypotheses.json
 ```
 Keep the input under about 12,000 tokens (one or a few related contracts). Larger inputs get
 ## Training
+LoRA on Qwen2.5-Coder-7B-Instruct, trained on High and Medium findings from 125 past Code4rena
 audits, paired with the code they were found in. The adapter is in `adapter/` if you want to
 merge or train further. Credit to the wardens who found the bugs.