---

license: apache-2.0

language:

- en

library_name: gguf

pipeline_tag: text-generation

tags:

- security

- vulnerability-detection

- agentic

- terminal-agent

- reinforcement-learning

- granite

- gguf

- lm-studio

- ollama

- llama-cpp

- quantized

base_model: fdtn-ai/antares-1b

---

Antraes-1B (GGUF)

Community GGUF quantizations of Cisco's Antares-1B — an open-weight 1-billion parameter language model specialized for vulnerability localization in real-world codebases. Built on IBM Granite 4.0 1B, it autonomously navigates source code repositories through a terminal interface using a two-stage SFT + GRPO pipeline.

These GGUF files enable running Antares-1B locally with llama.cpp, LM Studio, Ollama, Jan, Open WebUI, and other GGUF-compatible inference engines.

Model Details

| Property | Value |

|----------|-------|

| Model Developer | Cisco Systems, Inc. — Foundation AI (fdtn-ai/antares-1b) |

| Architecture | Auto-regressive decoder-only transformer (IBM Granite 4.0 1B backbone) |

| Parameters | 1B |

| Layers | 40, hidden dim 2048, 16 attention heads, 4 KV heads (GQA) |

| Context Window | 128K tokens |

| Vocabulary | 100,352 tokens |

| Activation | SwiGLU, RMSNorm, RoPE positional encoding |

| License | Apache 2.0 |

| Technical Report | Antares: Foundation Models for Agentic Vulnerability Localization |

| Training | SFT on cybersecurity reasoning + terminal-navigation data, followed by GRPO with verifiable rewards over multi-turn agent trajectories |

Performance

On the Vulnerability Localization Benchmark (VLoc Bench, 500 tasks), Antares-1B achieves a File F1 of 0.209, outperforming models many times its size including GLM-5.2, Gemini 3 Pro, GPT-5 Mini, and Qwen3.5-122B.

| Model | Parameters | File F1 | Precision | Recall |

|-------|-----------|---------|-----------|--------|

| Antares-1B (GRPO) | 1B | 0.209 | 0.262 | 0.224 |

| GLM-5.2 | 753B | 0.186 | 0.226 | 0.186 |

| Gemini 3 Pro | Frontier | 0.152 | 0.190 | 0.153 |

| Qwen3.5-122B-A10B | 125B MoE | 0.091 | 0.124 | 0.083 |

Files

| File | Format | Size | Description |

|------|--------|------|-------------|

| antraes-1b-f16.gguf | GGUF F16 | ~3.4 GB | Full-precision GGUF quantization |

| model.safetensors | SafeTensors | ~3.4 GB | Original PyTorch weights |

Usage

llama.cpp

# Build or download llama.cpp, then run:
./llama-cli -m antraes-1b-f16.gguf \
  --temp 0.3 \
  --top-p 1.0 \
  --ctx-size 8192 \
  -p "<|start_of_role|>system<|end_of_role|>You are a security vulnerability localization agent...<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Vulnerability to locate:\nCWE-78: OS Command Injection<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|><think>"

LM Studio

1. Download antraes-1b-f16.gguf
2. Open LM Studio → drag the .gguf file into the model directory
3. Select "Antraes-1B" from the model dropdown
4. This model uses the Granite chat template (auto-detected)

Ollama

# Create a Modelfile:
FROM ./antraes-1b-f16.gguf
TEMPLATE """{{ .System }}

{{ .Prompt }}"""

# Then:
ollama create antraes-1b -f Modelfile
ollama run antraes-1b

Jan

1. Download antraes-1b-f16.gguf
2. Open Jan → Settings → Extensions → enable "GGUF Engine"
3. Drag the .gguf file into Jan's model folder
4. The model will appear in your model list

Open WebUI

If using Ollama as the backend, pull the model into Ollama first (see above), then it will be available in Open WebUI.

Prompt Format

The model uses a structured tool-calling format with special tokens:

<|start_of_role|>system<|end_of_role|>...<|end_of_text|>
<|start_of_role|>user<|end_of_role|>...<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|><think>...reasoning...</think>
<tool_call>{"name": "terminal", "arguments": {"command": "..."}}</tool_call>
<|end_of_text|>
<|start_of_role|>user<|end_of_role|>
<tool_response>...output...</tool_response>
<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|><think>...

The agent loop terminates when the model calls submit_vulnerable_files or submit_no_vulnerability_found.

Intended Use

• Vulnerability Localization: Given a CWE identifier and a repository, identifying which source files contain the reported vulnerability using only a terminal interface
• Shift-Left Security: Integrating into CI/CD pipelines for early vulnerability detection
• Advisory-Driven Triage: Using CWE identifiers mapped from CVE/GHSA records to guide repository exploration

See the original model card for full details on intended/out-of-scope use, limitations, safety, and evaluation methodology.

Disclaimer

This is an unofficial community GGUF conversion of Cisco's Antares-1B model. These files are not affiliated with or endorsed by Cisco Systems, Inc. The original model is licensed under Apache 2.0 by Cisco Systems, Inc.