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intuitem
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Model Details
Model Provider
intuitem
Model Tree
mistralai/Ministral-3-14B-Instruct-2512
Input Modalities
Output Modalities
Supported Functionality
Out of scope: topics outside cybersecurity and IT (it declines), legal advice, and decisions taken without human review. Answers can be wrong: check them against the ANSSI guide.
Merged weights in safetensors (bf16, about 27 GB), with the chat template and Poulpe's default system prompt (SYSTEM_PROMPT.txt).
python
from transformers import pipeline chat = pipeline("text-generation", model="intuitem/Poulpe-Gen1-14B", device_map="auto", torch_dtype="auto")print(chat([{"role": "user", "content": "Quels sont les cinq ateliers d'EBIOS RM ?"}], max_new_tokens=400)[0]["generated_text"][-1]["content"])vLLM: vllm serve intuitem/Poulpe-Gen1-14B.
Without a system prompt, the chat template adds Poulpe's own default prompt, the one it was trained with. Recommended temperature: 0.2.
Scores between 0 and 1, held-out sets. Knowledge and facilitation sets are graded by an LLM judge against reference answers and the source pages; tool calling and JSON are checked deterministically. Judge noise is about ±0.02.
| Set | Items | This release | v4 | v4.1 |
|---|---|---|---|---|
| Closed-book EBIOS RM questions | 337 | 0.405 | 0.423 | 0.372 |
| True, false and partial premises | 92 | 0.403 | 0.407 | 0.362 |
| Workshop facilitation (multi-turn) | 36 | 0.528 | 0.517 | 0.530 |
| Driving a study with terse replies (multi-turn) | 8 | 0.611 | 0.425 | 0.595 |
| Identity, scope, general IT | 26 | 0.790 | 0.762 | 0.764 |
| Tool calling (CISO Assistant MCP tools, held out) | 36 | 0.986 | 0.963 | 0.899 |
| JSON output (schema-validated) | 10 | 1.000 | 1.000 | 1.000 |
| JSON with CISO Assistant's system prompt | 10 | 1.000 | 1.000 | 1.000 |
Fine-tuned with QLoRA (rank 16) on examples a teacher model (Gemma 4 31B, Apache-2.0) wrote from the documents below: question-answer pairs, corrections of false premises, multi-turn facilitation sessions, plus tool calls and general IT answers produced by the base model. The examples are reformulations, not the documents themselves. Formulas, rating scales and tables that the ANSSI documents only give as figures were transcribed by the teacher from the page images first, and the generated formulas were checked against them. About 40,000 training examples, one epoch on a single RTX 3090.
NOTICE-MITRE.txt). "© 2026 The MITRE Corporation. This work is reproduced and distributed with the permission of The MITRE Corporation."Report wrong answers or problems in the CISO Assistant issues, with the question, the answer and the expected one.
Model weights: Apache-2.0, as the base model. Source documents keep their own licences, listed above.
from transformers import pipeline chat = pipeline("text-generation", model="intuitem/Poulpe-Gen1-14B", device_map="auto", torch_dtype="auto")print(chat([{"role": "user", "content": "Quels sont les cinq ateliers d'EBIOS RM ?"}], max_new_tokens=400)[0]["generated_text"][-1]["content"])