---

license: apache-2.0

base_model:

• Qwen/Qwen3.5-0.8B

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• llama.cpp
• unsloth
• qwen3_5
• edge
• quantized
• conversational
• convergent-intelligence

---

Qwen3.5-0.8B Cyber — GGUF

A compact Qwen3.5 0.8B repository with a practical GGUF quantization ladder for local inference.

This card describes what is present in the repository. The public files do not document the fine-tuning dataset or provide evaluation results, so the Cyber label should be read as the repository variant name—not as a verified capability claim.

Repository contents

| Variant | Approximate size | Practical use |

|---|---:|---|

| BF16 | 1.52 GB | Highest-fidelity reference build |

| Q8_0 | 812 MB | High-quality local inference |

| Q6_K | 630 MB | Smaller high-quality option |

| Q4_K_M | 529 MB | Recommended size/quality starting point |

| Q2_K_L | 484 MB | Minimum footprint; expect greater quality loss |

| BF16-mmproj | 207 MB | Multimodal projector artifact; see caveat below |

Run locally

With a recent llama.cpp build:

llama-cli \
  -hf reaperdoesntknow/Qwen3.5-0.8-Cyber-GGUF:Q4_K_M \
  --jinja

With Ollama:

ollama run hf.co/reaperdoesntknow/Qwen3.5-0.8-Cyber-GGUF:Q4_K_M

Multimodal note

The repository includes a BF16 mmproj file and its configuration includes vision components. That establishes that a projector artifact is present; it does not establish that the end-to-end multimodal path was validated for this release. Verify image input locally before depending on it.

Intended use

• Local experimentation with a small Qwen3.5 model.
• CPU- and memory-constrained prototyping.
• Reproducible comparison of quantization levels.

Do not treat the model as an authoritative source for security, legal, medical, or operational decisions.

Evaluation status and limitations

• No benchmark results or comparison baselines are published in this repository.
• The fine-tuning dataset and training recipe are not documented in the public files reviewed for this card.
• Quantization reduces memory use and can change output quality.
• Model outputs may be incorrect, incomplete, biased, or unsafe.
• The multimodal path requires independent verification.

Lineage and license

• Base model: Qwen/Qwen3.5-0.8B
• Format: GGUF
• Conversion tooling recorded by the repository: Unsloth / llama.cpp
• License: Apache-2.0

Part of the CIx cybersecurity model collection.

<!-- cix-keeper-ts:2026-10-19T13:16:34Z -->