Upload DoctoModernBERT-fr-large-diffusion-v0.1

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LICENSE

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README.md

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+---
+language:
+  - fr
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - medical
+  - healthcare
+  - biomedical
+  - clinical
+  - french
+  - modernbert
+  - diffusion
+  - diffusion-language-model
+  - masked-diffusion
+  - dllm
+  - text-generation
+  - doctobert
+datasets:
+  - bofenghuang/medical-qa-fr-v0.1
+---
+
+# DoctoModernBERT-fr-large-diffusion-v0.1
+
+<p align="center">
+  <img src="assets/demo.gif" width="100%" alt="demo">
+</p>
+
+A text diffusion model based on [DoctoModernBERT-fr-large](https://huggingface.co/doctolib-lab/doctomodernbert-fr-large), finetuned on a medical instruction-following dataset.
+
+Unlike an autoregressive LLM, which decodes strictly left to right one token at a time, this model works by iterative denoising. It starts from a fully masked canvas and, at each step, predicts all masked positions at once and keeps only the most confident ones. It still moves through the sequence one block at a time, so generation is parallel inside each block but left to right across blocks.
+
+*This is an exploratory checkpoint. It's small and lightly trained, so expect mistakes and weaker answers.*
+
+## 🚀 How to Use
+
+Training and generation are done through the [dLLM](https://github.com/ZHZisZZ/dllm) library.
+
+### Setup
+
+```bash
+git clone https://github.com/ZHZisZZ/dllm
+cd dllm
+pip install -e .
+```
+
+### Sampling
+
+```python
+import dllm
+
+model_id = "bofenghuang/doctomodernbert-fr-large-diffusion-v0.1"  # or a local path
+
+model = dllm.utils.get_model(model_name_or_path=model_id).eval()
+tokenizer = dllm.utils.get_tokenizer(model_name_or_path=model_id)
+sampler = dllm.core.samplers.MDLMSampler(model=model, tokenizer=tokenizer)
+
+config = dllm.core.samplers.MDLMSamplerConfig(
+    steps=128,             # total diffusion steps
+    max_new_tokens=128,    # length of the generated answer span
+    block_size=32,         # semi-autoregressive block size
+    temperature=0.0,       # 0.0 = greedy / low-confidence remasking
+    remasking="low_confidence",
+)
+
+messages = [
+    [{"role": "user", "content": "Quels sont les symptômes typiques d'une infection urinaire ?"}],
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
+outputs = sampler.sample(inputs, config, return_dict=True)
+print(dllm.utils.sample_trim(tokenizer, outputs.sequences.tolist(), inputs)[0])
+```
+
+### Interactive chat
+
+```bash
+python -u examples/bert/chat.py --model_name_or_path "bofenghuang/doctomodernbert-fr-large-diffusion-v0.1"
+```
+
+The chat template wraps turns as `[SYS] … [/SYS]`, `[Question] … [/Question]`, `[Answer] … [/Answer]`.

chat_template.jinja

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+{% if messages[0]['role'] == 'system' %}
+[SYS]
+{{ messages[0]['content'] | trim }}
+[/SYS]
+
+{% set loop_messages = messages[1:] %}
+{% else %}
+{% set loop_messages = messages %}
+{% endif -%}
+{%- for message in loop_messages %}
+{% if message['role'] == 'user' %}
+[Question]
+{{ message['content'] | trim }}
+[/Question]
+
+{% elif message['role'] == 'assistant' %}
+[Answer]
+{{ message['content'] | trim }}
+[/Answer]
+
+{% endif %}
+{% endfor -%}
+{%- if add_generation_prompt and (loop_messages | length == 0 or loop_messages[-1]['role'] != 'assistant') %}
+[Answer]
+{% endif %}

config.json

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+{
+  "architectures": [
+    "ModernBertForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50283,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50283,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "bfloat16",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50281,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50282,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50281,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.56.0",
+  "vocab_size": 50368
+}

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+  ],
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+    "normalized": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
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+}

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