Upload dict-s

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+# IkhouDict-s
+
+## Model Description
+
+IkhouDict-s is a small bilingual dictionary model fine-tuned from
+Qwen/Qwen3-1.7B for single-line gloss generation. Given a word or short phrase
+in context, the model returns 1 to 4 short translations or synonyms in a target
+language. The rubric enforces a single line, no quotes or labels, no trailing
+punctuation, and optional French grammatical hints when the target language is
+French.
+
+## Intended Use
+
+This model is intended for lexicography support, language learning tools, and
+high-level draft glossing. It is not a substitute for professional translation
+or domain-specific terminology work. Outputs should be reviewed by humans in
+high-stakes settings.
+
+## Training Data
+
+Training data are produced by the data generation pipeline in `training/` in
+this repository. The pipeline creates synthetic dictionary examples from web
+corpora, then filters and formats them for supervised fine-tuning (SFT).
+
+Pipeline summary:
+
+1. Extract sentences from multilingual web corpora (FineWeb-2 by default; optional
+   FineWeb for English-only supplementation).
+2. Select a target word or phrase from each sentence (single token or short
+   phrase up to 5 tokens; `phrase_ratio` controls the mix).
+3. Sample target languages, including cross-lingual targets. The default config
+   uses 10 languages (`deu`, `eng`, `spa`, `fra`, `ita`, `jpn`, `kor`, `por`,
+   `rus`, `cmn`) and generates multiple target languages per example.
+4. A teacher LLM (OpenAI-compatible endpoint) generates a short gloss under a
+   strict rubric. Definitions are cleaned and validated.
+5. Examples below a quality threshold are dropped, then remaining examples are
+   de-duplicated by (source_lang, target_lang, selection, context).
+6. Each example is written to SFT JSONL format with a system prompt, a user
+   prompt, and a `<final>...</final>` assistant answer.
+
+The run used for this model produced:
+
+- Train: 1,521,749 examples
+- Eval: 15,366 examples
+- Test: 15,011 examples
+
+Splits are deterministic by grouping on provenance metadata to reduce leakage
+(see `sft/src/ikhou_sft/split.py`).
+
+## Training Procedure
+
+Fine-tuning was performed with the `ikhou_sft` pipeline in this repository:
+
+- Base model: Qwen/Qwen3-1.7B
+- Full fine-tuning (no LoRA)
+- Supervised fine-tuning using the chat template
+- Max sequence length: 512
+- Optimizer: Muon
+- 1 epoch with gradient accumulation
+
+See `sft/src/ikhou_sft/train.py` for implementation details.
+
+## How To Use
+
+The model expects a system prompt and a user prompt that mirror the data
+generation pipeline.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "ikhou/dict-s"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+system_prompt = (
+    "You are a bilingual dictionary assistant.\n\n"
+    "Your job: given a word/phrase in context, output a SHORT dictionary-style gloss line.\n\n"
+    "Hard rules:\n"
+    "- Output EXACTLY ONE LINE and nothing else.\n"
+    "- No quotes, no bullets, no labels (no \"Definition:\", \"Meaning:\", etc).\n"
+    "- Do NOT repeat the original word/phrase in the output.\n"
+    "- Keep it short (ideally <= 120 characters).\n\n"
+    "Gloss rules:\n"
+    "- Output 1-4 translations/synonyms in the definition language, separated by \", \".\n"
+    "- Each gloss should be short (1-3 words). Prefer common, user-friendly glosses.\n"
+    "- Do NOT write full sentences. No trailing period.\n\n"
+    "French grammar hints (only if confident):\n"
+    "IMPORTANT: The French-only formatting hints below apply ONLY when the definition language is French (fr/fra).\n"
+    "If the definition language is NOT French, do NOT use nm./nf./adj./adv., do NOT add French tense notes, and do NOT add (pp).\n"
+    "- Noun: prefix with \"nm.\" (masc) or \"nf.\" (fem), then a space, then glosses.\n"
+    "  Example: nm. face\n"
+    "- Adjective: prefix with \"adj.\", then a space, then glosses.\n"
+    "  Example: adj. fragile, delicate\n"
+    "- Adverb: prefix with \"adv.\", then a space, then glosses.\n"
+    "  Example: adv. extremely, exceedingly\n"
+    "- Conjugated verb form: glosses, then add \"(tense, subject)\" in French.\n"
+    "  Example: came back, used to come back (imparfait, il)\n"
+    "- Past participle: glosses, then add \"(pp)\".\n"
+    "  Example: watched over, supervised (pp)\n"
+)
+
+user_prompt = (
+    'Expression: "online"\n'
+    "Context: He paid for the course online and started immediately.\n"
+    "Source language: eng (English)\n"
+    "Definition language: spa (Spanish)\n\n"
+    "Return the single-line gloss now."
+)
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+)
+
+with torch.no_grad():
+    outputs = model.generate(
+        inputs,
+        max_new_tokens=64,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded)
+```
+
+## Limitations and Risks
+
+- Outputs can be inaccurate, overly general, or inconsistent with the rubric.
+- The model inherits biases from source corpora and the teacher model.
+- Rare languages or specialized terminology may be poorly handled.
+
+## Acknowledgements
+
+Base model: Qwen/Qwen3-1.7B.

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.3"
+}

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special_tokens_map.json

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+{
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+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+    "single_word": false
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+    "single_word": false
+  }
+}

tokenizer.json

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+size 11422654

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+    "<|image_pad|>",
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+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

training_args.bin

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+size 5777