Upload folder using huggingface_hub

.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

@@ -0,0 +1,203 @@
+---
+language:
+  - ko
+  - en
+license: apache-2.0
+base_model: unsloth/Qwen3-4B-Instruct-2507
+tags:
+  - lora
+  - peft
+  - bitext
+  - chunking
+  - translation
+  - alignment
+  - sft
+  - unsloth
+  - trl
+  - transformers
+library_name: peft
+pipeline_tag: text-generation
+---
+
+# Chunky — Bitext Chunk Alignment Model (LoRA)
+
+A LoRA adapter fine-tuned on **Qwen3-4B-Instruct-2507** for the task of finding optimal split points in parallel bilingual text (bitext chunking). Given a source and target text pair with pre-inserted split markers, the model predicts which pairs of split indices align semantically.
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/p4b/huggingface/runs/fjw39wif)
+
+## Task
+
+Given `<src>` and `<tgt>` blocks with numbered split markers `[|1|]`, `[|2|]`, ..., predict the optimal alignment pairs as `<answer>src_idx-tgt_idx, ...</answer>`.
+
+**Example input:**
+
+```
+<src>Document title[|1|]First paragraph content.[|2|]Second paragraph.</src>
+<tgt>문서 제목[|1|]첫 번째 단락 내용.[|2|]두 번째 단락.</tgt>
+```
+
+**Expected output:**
+
+```
+<answer>1-1, 2-2</answer>
+```
+
+## Model Details
+
+| Property             | Value                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Base model           | `unsloth/Qwen3-4B-Instruct-2507`                              |
+| Method               | SFT with LoRA (PEFT)                                          |
+| LoRA rank            | 32                                                            |
+| LoRA alpha           | 64                                                            |
+| Target modules       | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Training precision   | fp32                                                          |
+| Training steps       | 8000 (best checkpoint: step 7000)                             |
+| Max sequence length  | 12800 tokens                                                  |
+| Training samples     | ~316k augmented bitext alignment samples (Korean–English)     |
+| Optimizer            | AdamW (lr=2e-4, warmup=5%, cosine decay)                      |
+| Effective batch size | 8 (4 × grad_accum 2)                                          |
+| Best eval loss       | 0.03788 (step 7000)                                           |
+| Final eval loss      | 0.03844 (step 8000)                                           |
+
+### Framework Versions
+
+- PEFT 0.18.1
+- TRL 0.23.0
+- Transformers 4.56.2
+- PyTorch 2.9.1
+- Unsloth 2026.4.4
+
+## Usage
+
+### With Unsloth (recommended)
+
+```python
+from unsloth import FastLanguageModel
+from unsloth.chat_templates import get_chat_template
+from peft import PeftModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "unsloth/Qwen3-4B-Instruct-2507",
+    max_seq_length=12800,
+    load_in_4bit=False,
+)
+tokenizer = get_chat_template(tokenizer, chat_template="qwen3-instruct")
+model = PeftModel.from_pretrained(model, "p4b/chunky-qwen3-4b-sft")
+model = FastLanguageModel.for_inference(model)
+```
+
+### With standard transformers + PEFT
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+base_model = "unsloth/Qwen3-4B-Instruct-2507"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.bfloat16)
+model = PeftModel.from_pretrained(model, "p4b/chunky-qwen3-4b-sft")
+model.eval()
+```
+
+### Inference Example
+
+```python
+SYSTEM_PROMPT = """## Task Description
+You are a Linguistic Structure Analyst and Translation Alignment Expert. Your task is to analyze the provided `<src>` (source) and `<tgt>` (target) blocks to identify "Optimal Split Points.". Each split should be in closed form when translating bidirectionally. You should carefully look for pronoun relation or tense.
+
+## Task Objective
+Find the index numbers `[|n|]` where the text can be naturally divided into two parts. A split is considered "optimal" if the segments before and after the split remain independently understandable and do not break the semantic flow.
+
+## Guidelines for Selection
+1. **Structural Cues**: Prioritize indices located next to structural markers, such as hyphens (`-`), bullet points, or section dividers.
+2. **Contextual Independence**: The content after the split point should start a new logical section or thought (e.g., a new heading or a different category of information).
+3. Example Logic: In the text `...Information [|32|]-[|33|] Nearby...`, the index `[|33|]` is an ideal split point because it follows a hyphen and precedes a new sub-topic.
+4. Alignment: Match the corresponding split point index from the `<src>` block with the equivalent split point index in the `<tgt>` block.
+
+## Constraint
+- The output must be formatted strictly as: `<answer>SourceIndex-TargetIndex, SourceIndex-TargetIndex</answer>`
+
+## Input
+"""
+
+def insert_split_tokens(chunks: list[str]) -> str:
+    parts = []
+    for i, chunk in enumerate(chunks, start=1):
+        parts.append(chunk)
+        parts.append(f"[|{i}|]")
+    return "".join(parts)
+
+src_chunks = ["Introduction paragraph.", "Main content section.", "Conclusion."]
+tgt_chunks = ["서론 단락.", "본문 내용 섹션.", "결론."]
+
+text = f"<src>{insert_split_tokens(src_chunks)}</src><tgt>{insert_split_tokens(tgt_chunks)}</tgt>"
+messages = [{"role": "user", "content": SYSTEM_PROMPT + text}]
+
+input_text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = tokenizer(input_text, return_tensors="pt", add_special_tokens=False).to(model.device)
+
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=False,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+response = tokenizer.decode(
+    output_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
+)
+print(response)  # <answer>2-2</answer>
+```
+
+## Training Data
+
+Fine-tuned on augmented bitext alignment samples generated from Korean–English parallel corpora. Augmentation was applied to increase diversity in split-point configurations. Training used the augmented subset only (~316k samples), leaving the original ~105k samples as an unseen evaluation pool.
+
+## Evaluation Metrics
+
+Evaluated using FN/FP reward scoring (same as GRPO training objective):
+
+| Metric              | Description                           | Weight |
+| ------------------- | ------------------------------------- | ------ |
+| FN (false negative) | Missed ground-truth split pairs       | 1.0    |
+| FP (false positive) | Predicted pairs not in ground truth   | 0.2    |
+| Length penalty      | Predicted segments >3× longer than GT | 1.0    |
+| Reward              | `-(1.0×FN + 0.2×FP + length_penalty)` | —      |
+
+### Evaluation Results
+
+Evaluated on 300 randomly sampled held-out original (non-augmented) samples from `train_split.jsonl`:
+
+| Metric               | Value               |
+| -------------------- | ------------------- |
+| Perfect reward (=0)  | **53.7%** (161/300) |
+| Reward mean / median | -1.654 / 0.000      |
+| Reward stdev         | 3.277               |
+| FN mean              | 1.393               |
+| FP mean              | 0.687               |
+| Length penalty mean  | 0.070               |
+| Parse error rate     | 5.3%                |
+
+**Reward distribution:**
+
+| Range         | Count | %     |
+| ------------- | ----- | ----- |
+| = 0 (perfect) | 161   | 53.7% |
+| -1 ~ 0        | 3     | 1.0%  |
+| -2 ~ -1       | 70    | 23.3% |
+| -5 ~ -2       | 43    | 14.3% |
+| ≤ -5          | 23    | 7.7%  |
+
+The median reward is 0 — the majority of samples are predicted perfectly. The mean is pulled down by a small number of hard samples with many chunks (100+).
+
+## Limitations
+
+- Primarily trained on Korean–English parallel text; other language pairs are untested.
+- May underperform on documents longer than 12800 tokens.
+- Trained without thinking/reasoning mode — purely output-direct fine-tuning.

adapter_config.json

@@ -0,0 +1,50 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen3ForCausalLM",
+    "parent_library": "transformers.models.qwen3.modeling_qwen3",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/Qwen3-4B-Instruct-2507",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "up_proj",
+    "o_proj",
+    "k_proj",
+    "down_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa858c604c32a3a063bb64162346426b3a09aa60f70a68c94bf93b654ef19a5d
+size 264308896

added_tokens.json

@@ -0,0 +1,29 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|PAD_TOKEN|>": 151669,
+  "<|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

@@ -0,0 +1,86 @@
+{%- 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 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' }}
+{%- endif %}

special_tokens_map.json

@@ -0,0 +1,25 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|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|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|PAD_TOKEN|>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df63e89fb86f987a2c2342b0a59ad219962f459786d504dcf561bed0b4a803a2
+size 11422944

tokenizer_config.json

@@ -0,0 +1,248 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151669": {
+      "content": "<|PAD_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|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|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 262144,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0468010e56f1526b7ccacb4cb194bf5a8ad05599f4815225eb2c8e9e8af1f518
+size 6289