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Model: FrenchCastle/sexology-v4
Source: Original Platform

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README.md

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+---
+# 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]

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+    "model.layers.8.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "model.layers.8.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.8.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.input_layernorm.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "model.norm.weight": "model-00003-of-00003.safetensors"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,35 @@
+{
+  "additional_special_tokens": [
+    "<unk>",
+    "<s>",
+    "</s>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,48 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<unk>",
+    "<s>",
+    "</s>"
+  ],
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "truncation_side": "left",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true
+}