blueteam-cyber-base-v0.3.0
A roughly 100.7M-parameter Llama-compatible base model
pretrained from scratch: a mixture of general English, code,
cybersecurity text, defensive code-review examples, and Open Interpreter-style traces.
This is a base completion model, not an instruction-tuned assistant. Evaluate it as a
text continuation model before applying supervised or preference fine-tuning.
Fine-tuning status: none. This repository contains the raw pretrained base
checkpoint. It has not received supervised instruction fine-tuning, preference
optimization, vulnerability-report fine-tuning, tool-call fine-tuning, or Open
Interpreter integration. It should not be presented as a reliable security reviewer or
autonomous agent.
Training summary
- Parameters: 100,682,496
- Training step: 57,308 / 57,308
- Tokens processed: 1,877,868,544
- Context used for pre-training: 1,024
- Prepared training split: 1,877,928,931 custom-tokenizer tokens
- Tokens packable at the configured sequence length: 1,877,897,216
- Checkpoint slot: final
Base-model evaluation
Native evaluation completed on August 17, 2026:
- Full prepared holdout: 20,008 sequences / 20,488,192 target tokens
- Holdout coverage: 100% of packable targets
- Cross-entropy loss: 1.9503
- Perplexity: 7.0310
- Qualitative result: partially coherent security vocabulary, but substantial repetition,
factual errors, and broken code generation
- Two-case structured security format check: 0% valid JSON and 0% verdict accuracy
The security check was only one vulnerable/patched SQL-injection pair and is a pipeline
demonstration, not a statistically meaningful benchmark. The result confirms that this
base checkpoint requires supervised instruction and report-format fine-tuning before it
can be evaluated as a code-review assistant or tool-using agent.
GGUF and Ollama
blueteam-cyber-base-v0.3.0-Q8_0.gguf is the portable llama.cpp/Ollama build:
- Format: GGUF V3 / Q8_0
- Size: 107,838,208 bytes (102.07 MiB)
- SHA-256:
4c6b00c9c4a84e04691f904638f68f5aaf00d418b0dff4c783952e22dccca45f
- Converter: official llama.cpp revision
d8df12ebc4592b55dcecf97a32953623d031efdc
- Validation: loaded by Ollama 0.13.5, all 13 layers offloaded to the RTX 3060, and a
bounded generation smoke test completed successfully
Download the GGUF and Modelfile into the same directory, then run:
1ollama create blueteam-cyber-base-v0.3.0 -f Modelfile
2ollama run blueteam-cyber-base-v0.3.0
This only changes the runtime format. Quantization does not add instruction-following,
chat, vulnerability-review, or tool-use capabilities.
This corpus contains security-related code and strings that can trigger malware scanners,
but the training pipeline treats JSONL content as inert text. The security/agent/report
portions are predominantly synthetic. Test factuality, memorization, security behavior,
and code quality before downstream use.
