--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards license: apache-2.0 language: - fr library_name: transformers pipeline_tag: text-generation base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - sexual-health - sexology - sante-sexuelle - health - medical - public-health - french - conversational - instruction-tuned --- # Model Card for Sexo-FR **Sexo-FR** is a French-language conversational language model that provides reliable, caring, and evidence-based **sexual health information** (information en santé sexuelle). It is part of a French public-health initiative whose goal is to make trustworthy sexual-health information more accessible to the public, in plain French, while consistently redirecting users to qualified professionals and dedicated helplines. The model is an **informational and educational tool**. It is **not** a medical device, it does **not** provide diagnoses, and it does **not** replace consultation with a healthcare professional (doctor, midwife, sexologist, psychologist, family-planning counsellor, etc.). ## Model Details ### Model Description Sexo-FR is an instruction-tuned causal language model specialised for the sexual-health domain in French. It was fine-tuned from a general-purpose open base model on a curated corpus of French-language question/answer and instruction data covering topics such as contraception, sexually transmitted infections (IST), consent, sexual and reproductive health, relationships, sexual well-being, and inclusive information on gender identity and sexual orientation. The training emphasised three things: (1) **factual reliability**, with content aligned to recognised public-health and clinical guidance; (2) **a caring, non-judgemental and inclusive tone**; and (3) **safe behaviour**, including refusing to produce sexually explicit content, recognising distress or emergencies, and systematically signposting appropriate professional resources and helplines. - **Developed by:** [French public-health body — e.g., *Santé publique France* — to confirm] - **Funded by [optional]:** Government of France — [e.g., *Ministère de la Santé et de la Prévention* — to confirm] - **Shared by [optional]:** [Same as above / project team — to confirm] - **Model type:** Decoder-only (causal) transformer language model, instruction-tuned for domain-specific conversational use - **Language(s) (NLP):** French (`fr`) - **License:** `apache-2.0` (inherited from the base model). *Note:* documentation, prompts, and any released data may additionally be published under the French government open licence (**Licence Ouverte / Etalab 2.0**). Confirm the final licensing strategy with your legal team before release. - **Finetuned from model [optional]:** [`mistralai/Mistral-7B-Instruct-v0.3` — confirm exact base model and revision] ### Model Sources [optional] - **Repository:** [https://huggingface.co/your-org/sexo-fr — to add] - **Paper [optional]:** [Link or DOI, if any — to add] - **Demo [optional]:** [Link to the official public service / demo — to add] ## Uses ### Direct Use Sexo-FR is intended to power a French-language assistant that answers general sexual-health questions and points people to appropriate care. Representative in-scope topics include: - **Contraception:** available methods, emergency contraception, where and how to obtain them. - **Sexually transmitted infections (IST):** prevention, awareness of symptoms, screening (*dépistage*), prevention tools (condoms, PrEP), and how to seek testing or treatment. - **Consent and healthy relationships** (*consentement*): understanding consent, communication, recognising coercion or violence. - **Sexual and reproductive health:** anatomy and physiology, pregnancy, contraception, and information about IVG (voluntary termination of pregnancy). - **Sexual well-being and common difficulties:** general, non-clinical information about desire, pleasure, pain, or function — always with a recommendation to consult a professional for personal situations. - **Life stages:** puberty and adolescence, menopause, and sexuality with age, disability, or chronic illness. - **Identity and inclusion:** respectful, inclusive information regarding sexual orientation and gender identity (LGBTQ+). - **Prevention and support:** recognising sexual violence and signposting dedicated support services. Typical deployments: a chatbot or Q&A feature on an official public-health website, an FAQ-assistance layer, or an internal drafting aid reviewed by human experts. ### Downstream Use [optional] The model can be further adapted for specific public-health programmes, for example: - Fine-tuning on additional, expert-validated content for a particular campaign or audience (e.g., youth, new parents, specific clinical pathways). - Integration into a retrieval-augmented (RAG) system grounded in an authoritative, regularly updated knowledge base so that answers cite up-to-date official sources. - Use as an assistant for trained counsellors and helpline operators, with human review before any message is sent to a member of the public. Any downstream use should preserve the safety behaviours described below and undergo renewed evaluation. ### Out-of-Scope Use This model must **not** be used for: - **Medical diagnosis, treatment decisions, prescriptions, or dosing.** It is not a medical device and provides general information only. - **Emergency or crisis situations.** It is not designed to handle medical emergencies, acute distress, suicidal ideation, or ongoing violence/abuse. Such cases must be routed to emergency services and dedicated helplines. - **Replacing professional care** by a doctor, midwife, sexologist, psychologist, or family-planning service. - **Generating sexually explicit, pornographic, or arousal-oriented content.** The model is for health information and education, not erotica. - **Unsupervised use with minors.** Deployments accessible to adolescents must align with national sexual-education standards, remain age-appropriate, and route to youth-appropriate services. The model must never be used to facilitate any sexualisation of, or harm to, minors. - **Legal advice** (e.g., precise statutory or regulatory determinations). - **Use outside the French language and the French health/care context** without separate validation. - **Profiling, surveillance, or any re-identification** of users based on the sensitive content of their questions. ## Bias, Risks, and Limitations This model operates in a **sensitive and high-stakes domain**. Incorrect, incomplete, or poorly framed information about sexual health can cause real harm. The following limitations are inherent and must be communicated to users and deployers. **Technical limitations** - **Hallucination and factual error.** Like all LLMs, the model can produce plausible-sounding but incorrect statements, including about medical facts. It can also reflect outdated guidance, since its knowledge is frozen at training time and recommendations evolve. - **No source guarantees.** Unless deployed with retrieval grounding, the model cannot reliably cite or verify sources. - **Sensitivity to phrasing.** Answers may vary with prompt wording; ambiguous questions may be misinterpreted. - **Language scope.** The model targets standard French; it may handle regional variants, slang, mixed languages, or non-French input less reliably. **Sociotechnical risks and biases** - **Cultural and demographic bias.** Training data may over-represent certain perspectives and under-represent others (e.g., LGBTQ+ experiences, people with disabilities, diverse cultural or religious contexts). The model may carry heteronormative or other normative biases. - **France-centric framing.** Guidance, services, and helplines referenced may be specific to France and not transferable elsewhere. - **Tone and stigma.** Despite tuning for a non-judgemental tone, the model could occasionally produce content that feels stigmatising or insufficiently inclusive. - **Privacy and sensitivity.** Users may disclose highly intimate or identifying information. Deployers are responsible for data minimisation, secure handling, and compliance with the GDPR and applicable French regulations. - **Over-reliance.** Users may treat answers as personal medical advice. Clear, persistent disclaimers and easy routes to human help are essential. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. In addition: - **Always present the model as informational, not a substitute for professional care**, with a visible disclaimer at the point of use. - **Keep a human in the loop** for any individual situation, clinical question, or message sent to a vulnerable user. - **Prefer retrieval-grounded deployment** against an authoritative, current knowledge base, and review content periodically against the latest official guidance. - **Surface professional resources and helplines** prominently, and verify these contacts are current for your jurisdiction. Examples of French resources to consider signposting (please verify numbers before release): - Emergencies: **15** (SAMU), **112** (European emergency number), **114** (emergency by SMS, for deaf/hard-of-hearing). - Suicide prevention: **3114** (national prevention line). - Violence against women: **3919** (*Violences Femmes Info*). - Child protection: **119** (*Allô Enfance en Danger*). - Youth sexual-health information: *Fil Santé Jeunes*. - General sexual-health information: a public, official sexual-health information service (e.g., a `questionsexualite.fr`-type resource). - HIV/IST information and support: a dedicated national IST/HIV information line. - Family planning / contraception / IVG information services. - **Apply content safeguards** to refuse explicit-content requests, detect distress, and escalate appropriately. - **Re-evaluate after any fine-tuning or major integration change.** ## How to Get Started with the Model The model uses a standard chat/instruction format. A system prompt should encode the informational-only, caring, and safety-first behaviour. Always verify the chat template of your chosen base model. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "your-org/sexo-fr" # replace with the published repo id tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) system_prompt = ( "Tu es un assistant d'information en santé sexuelle conçu dans le cadre " "d'une initiative de santé publique française. Tu fournis des informations " "fiables, bienveillantes, inclusives et fondées sur des données probantes, " "en français clair. Tu ne poses jamais de diagnostic et tu ne remplaces pas " "un professionnel de santé. Tu ne produis aucun contenu sexuellement " "explicite. En cas d'urgence, de détresse ou de violence, tu orientes vers " "les services et numéros d'aide appropriés." ) messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "Quelles sont les principales méthodes de contraception ?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=512, temperature=0.3, top_p=0.9, do_sample=True, ) print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)) ``` A low temperature (e.g., 0.2–0.4) is recommended to favour factual, conservative answers in this domain. ## Training Details ### Training Procedure #### Training Hyperparameters The following are representative values for a parameter-efficient (LoRA/QLoRA) supervised fine-tune on L40 GPUs — **replace with your actual configuration**: - **Training regime:** bf16 mixed precision - **Method:** Supervised fine-tuning (SFT) with LoRA (optionally an additional preference-optimisation pass, e.g., DPO, for safety/tone) - **LoRA config (example):** rank `r = 16`, `alpha = 32`, dropout `0.05`, applied to attention/MLP projection layers - **Optimizer:** AdamW (paged 8-bit if using QLoRA) - **Effective batch size (example):** `32` (per-device batch × gradient accumulation) - **Max sequence length (example):** `2048` ## Evaluation ### Factors & Metrics #### Factors Evaluation should be disaggregated by, at minimum: - **Topic** (contraception, IST, consent, reproductive health, identity, well-being, etc.) - **Question type** (factual, procedural/"where do I go", sensitive/personal, safety-critical) - **Population/inclusivity** (e.g., LGBTQ+ relevant queries, different life stages) - **Risk category** (general information vs. safety-critical vs. out-of-scope) #### Metrics - **Factual accuracy / clinical correctness** — expert rating of answers against recognised guidance (primary metric in this domain). - **Safety** — rate of appropriate refusals for explicit-content/harmful requests and appropriate redirection to professionals/helplines; absence of explicit content. - **Appropriate signposting** — proportion of answers that direct users to professional care or relevant services when warranted. - **Helpfulness & clarity** — readability and usefulness, rated by reviewers. - **Inclusivity & tone** — non-judgemental, inclusive language. - **Coverage** — performance breadth across topics. #### Summary [Add a short narrative summary of strengths and known weaknesses once evaluation is complete.] ## Model Examination [optional] [Optional.] Any interpretability or qualitative-analysis work — e.g., review of failure cases, audits of bias on inclusivity-relevant queries, or red-teaming findings — can be summarised here. ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The figures below are **illustrative**; replace them with your measured values. - **Hardware Type:** NVIDIA L40 (48 GB GDDR6, ~300 W TDP) - **Hours used:** [e.g., 192 GPU-hours — to confirm] - **Cloud Provider:** [e.g., on-premise / French sovereign-cloud (SecNumCloud) provider — to confirm] - **Compute Region:** France - **Carbon Emitted:** [e.g., ~4–5 kg CO₂eq — illustrative] > *Note on the estimate:* France's electricity grid is relatively low-carbon (broadly on the order of ~55–60 gCO₂eq/kWh, varying over time). For example, ~192 GPU-hours at ~0.3 kW, with a data-centre PUE of ~1.3, gives roughly ~75 kWh and on the order of ~4 kg CO₂eq. Substitute your real GPU-hours, PUE, and grid intensity for a precise figure. ## Technical Specifications [optional] ### Model Architecture and Objective Decoder-only transformer (causal language model), trained with a standard next-token prediction objective during instruction fine-tuning. [Confirm parameter count and architecture from the chosen base model — e.g., ~7B parameters for Mistral 7B.] ### Compute Infrastructure #### Hardware NVIDIA L40 GPUs. #### Software PyTorch, Hugging Face `transformers`, `peft` (LoRA/QLoRA), `trl` for SFT/DPO, `accelerate`/`deepspeed` for distributed training. **APA:** [Developing organisation]. ([Year]). *Sexo-FR: A French sexual-health information language model* [Computer software/model]. Hugging Face. https://huggingface.co/your-org/sexo-fr ## Glossary [optional] - **IST** — *Infections sexuellement transmissibles* (sexually transmitted infections, STIs). - **IVG** — *Interruption volontaire de grossesse* (voluntary termination of pregnancy). - **PrEP** — Pre-exposure prophylaxis (HIV prevention). - **Dépistage** — Screening/testing. - **Consentement** — Consent. - **RAG** — Retrieval-augmented generation: grounding model answers in an external, authoritative knowledge base. ## More Information [optional] This model is an informational and educational tool only. It does not provide medical advice, diagnosis, or treatment and does not replace consultation with a qualified professional. In an emergency, contact local emergency services. [Add programme details, governance, and contact channels here.] ## Model Card Contact [francois@chastel.co]