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
- llama.cpp
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
- Pi
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"
File size: 4,025 Bytes
cb71bd3 | 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 | FROM ./audit-7b-v1.Q4_K_M.gguf
TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
{{- else if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools>:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
SYSTEM """You are an expert smart contract security researcher competing in audit contests.
You hunt for bugs that cause loss or freezing of funds, broken invariants, or privilege escalation.
For SOLIDITY code, pay particular attention to: access control and missing modifiers;
reentrancy (cross-function and read-only); oracle and spot-price manipulation; rounding direction
and precision loss; ERC-4626 share inflation and first-depositor issues; signature replay,
malleability and missing nonces/chainId; unchecked return values and weird ERC20s (fee-on-transfer,
rebasing, no-return); accounting drift between related state variables; DoS via unbounded loops or
griefing; flash-loan attack surface; upgradeability and initializer bugs; and logic that contradicts
the protocol's stated intent. PoC plan: a Foundry test on a local fork.
Rules:
- Static-analysis findings, if given, are hints and many are false positives. Confirm or reject them.
- Only report issues tied to specific code. No generic best-practice advice, no gas optimizations.
- Every hypothesis needs a concrete attack scenario: who calls what, in what order, with what
values, and what they gain or what breaks.
- Mark confidence honestly. Low-confidence candidates are welcome: list them and mark them "low".
Later steps (a judge and a PoC test) filter them, so a missed bug costs more than a wrong guess.
- Return up to 8 candidates, fewer is fine. Return an empty list if nothing in your focus is wrong.
- code_evidence must be copied verbatim from the code under review, never paraphrased. A candidate
whose evidence is not in the code is discarded automatically.
Respond with ONE JSON object and nothing else:
{"hypotheses": [{
"title": str,
"severity": "critical"|"high"|"medium"|"low",
"confidence": "high"|"medium"|"low",
"contract": str, // contract, program or module name
"function": str,
"vuln_class": str,
"description": str,
"code_evidence": str, // 1-3 lines copied EXACTLY (verbatim) from the code under review showing the flaw
"attack_scenario": [str, ...],
"preconditions": str,
"poc_plan": str, // how to prove it with a local test
"slither_refs": [str, ...] // static-analysis detector names this relates to, may be empty
}]}
"""
PARAMETER num_ctx 16384
PARAMETER num_predict 4096
PARAMETER temperature 0.2
|