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Model: KordAI/Kord-Translate-ENTH-V2-4B
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+- th
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+tags:
+- translation
+- thai
+- english
+- machine-translation
+- qwen3
+- distillation
+datasets:
+- KordAI/Translation-Pairs-8K
+pipeline_tag: translation
+---
+
+# Kord Translate ENTH V2 — 4B
+
+Bidirectional Thai ⇄ English translation model, fine-tuned from **Qwen3-4B** via **rationale-free distillation** from a large reasoning teacher (DeepSeek-V4-Flash).
+
+This model is part of the Kord Translate ENTH V2 family, accompanying the paper *"Teaching the Student to Skip the Homework: Rationale-Free Distillation for Thai-English Translation"* (KordAI, 2026). Other models in the family: [1.7B](https://huggingface.co/KordAI/Kord-Translate-ENTH-V2-1.7B), [8B](https://huggingface.co/KordAI/Kord-Translate-ENTH-V2-8B), [mBART50](https://huggingface.co/KordAI/Kord-Translate-MBART50-ENTH-V2).
+
+## Model Description
+
+- **Base model:** Qwen3-4B
+- **Adaptation:** LoRA (rank 8, alpha 16, dropout 0.02) applied to all attention and MLP projection matrices, trained in 4-bit precision with gradient checkpointing (Unsloth)
+- **Training data:** [`KordAI/Translation-Pairs-8K`](https://huggingface.co/datasets/KordAI/Translation-Pairs-8K) — ~8,000 bidirectional Thai/English pairs generated by prompting DeepSeek-V4-Flash through an explicit 4-stage reasoning procedure (literal meaning → genre/formality → vocabulary/honorifics → natural rewrite), keeping **only the final translation** and discarding the reasoning trace
+- **Loss masking:** assistant-only, so gradients only flow through the translation output, not the system prompt or source text
+- **Epochs:** 3, LoRA learning rate 2e-4 (cosine decay, 5 warmup steps), paged AdamW 8-bit optimizer
+- **Compute:** 1× NVIDIA Tesla T4 (16GB), per-device batch 4, gradient accumulation 32
+
+## Results (FLORES devtest, 1,012 samples/direction)
+
+| Direction | Model | BLEU | chrF | chrF++ | BERTScore-P | BERTScore-F1 | COMET |
+|---|---|---|---|---|---|---|---|
+| en→th | Qwen3-4B (base) | 8.87 | 48.19 | 39.87 | 0.82 | 0.82 | 0.86 |
+| en→th | **Kord Translate 4B** | **9.46** | **49.01** | **40.57** | 0.82 | 0.82 | 0.86 |
+| th→en | Qwen3-4B (base) | 26.94 | 56.79 | 54.39 | 0.95 | 0.95 | 0.87 |
+| th→en | **Kord Translate 4B** | 25.74 | 55.79 | 53.34 | 0.95 | 0.95 | 0.87 |
+
+Rationale-free distillation produces a small but clear BLEU/chrF gain on en→th (+0.59 BLEU), while th→en is essentially flat to slightly down relative to the untuned Qwen3-4B base — consistent with the paper's finding that gains shrink as base model competence increases. See the paper for comparison against other scales and specialized Thai-English systems.
+
+## Inference
+
+This is a chat/instruction-tuned model. Prompt with a system message asking for translation and a user message containing the source text.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "KordAI/Kord-Translate-ENTH-V2-4B"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+def translate(text: str, direction: str = "en2th") -> str:
+    """direction: 'en2th' or 'th2en'"""
+    src_lang, tgt_lang = ("English", "Thai") if direction == "en2th" else ("Thai", "English")
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                f"You are a professional {src_lang}-{tgt_lang} translator. "
+                f"Translate the user's text from {src_lang} to {tgt_lang}. "
+                "Output only the translation, with no explanation, notes, or extra text."
+            ),
+        },
+        {"role": "user", "content": text},
+    ]
+
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            do_sample=False,
+            temperature=None,
+            top_p=None,
+            top_k=None,
+        )
+
+    generated = output_ids[0][inputs["input_ids"].shape[-1]:]
+    return tokenizer.decode(generated, skip_special_tokens=True).strip()
+
+
+print(translate("How is the weather today in Bangkok?", direction="en2th"))
+print(translate("วันนี้อากาศที่กรุงเทพเป็นอย่างไรบ้าง", direction="th2en"))
+```
+
+**Using Unsloth (faster 4-bit inference, matches training setup):**
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="KordAI/Kord-Translate-ENTH-V2-4B",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)  # enable native 2x faster inference
+
+messages = [
+    {"role": "system", "content": "You are a professional English-Thai translator. Translate the user's text from English to Thai. Output only the translation, with no explanation, notes, or extra text."},
+    {"role": "user", "content": "How is the weather today in Bangkok?"},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
+).to("cuda")
+
+output_ids = model.generate(input_ids=inputs, max_new_tokens=256, do_sample=False)
+print(tokenizer.decode(output_ids[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+---
+
+## Sample Translations
+
+### English → Thai
+
+| Source | Translation |
+|--------|-------------|
+| *Ring also settled a lawsuit with competing security company, the ADT Corporation.* | ริงยังได้ตัดสินพิจารณาคดีกับบริษัทความปลอดภัยที่แข่งขันกันอย่าง ADT Corporation  |
+| *USA Gymnastics and the USOC have the same goal — making the sport of gymnastics, and others, as safe as possible for athletes to follow their dreams in a safe, positive and empowered environment.* | USA Gymnastics และ USOC มีเป้าหมายเดียวกัน คือ การทำให้กีฬากรีฑศาสตร์และกีฬาอื่นๆ เป็นกีฬาที่ปลอดภัยที่สุดเท่าที่จะทำได้สำหรับนักกีฬาที่จะได้ไปตามฝันของพวกเขาในสภาพแวดล้อมที่ปลอดภัย บวก สร้างสรรค์ และมีอำนาจ |
+
+### Thai → English
+
+| Source | Translation |
+|--------|-------------|
+| *แกงอาจมีทั้งชนิด "แห้ง" หรือ "น้ำ" ขึ้นอยู่กับปริมาณของเหลว* | There are both dry and wet types of curry, depending on the amount of liquid used. |
+| *เนื่องจากมีหมู่เกาะให้เลือกถึง 17,000 เกาะ คำว่าอาหารอินโดนีเซียจึงเป็นคำเรียกกว้าง ๆ ที่ครอบคลุมถึงอาหารประจำภูมิภาคทั่วประเทศ* | Because there are 17,000 islands to choose from, the term \"Indonesian food\" is a broad term that encompasses the cuisine of all regions in the country. |
+
+---
+
+## Limitations
+
+- Trained on a small (~8K pair), single-teacher distillation set; may not generalize to document-level or highly colloquial Thai.
+- Evaluated only on FLORES devtest (sentence-level general-domain text).
+- Gains over the untuned Qwen3-4B base are modest and direction-dependent (positive on en→th, roughly flat on th→en).
+
+## Citation
+
+```bibtex
+@article{kordai2026rationalefree,
+  title   = {Teaching the Student to Skip the Homework: Rationale-Free Distillation for Thai-English Translation},
+  author  = {Jangjit, Naphon and Komsang, Jeerawat and Boran, Kord C.},
+  year    = {2026},
+  organization = {KordAI}
+}
+```
+
+## Acknowledgements
+
+Built on [Qwen3](https://arxiv.org/abs/2505.09388), with teacher supervision from [DeepSeek-V4](https://arxiv.org/html/2606.19348v1). LoRA fine-tuning follows [Hu et al., 2021](https://arxiv.org/abs/2106.09685) and the 4-bit recipe popularized by [QLoRA](https://arxiv.org/abs/2305.14314).
+
+This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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 forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = message.content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (message.content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = message.content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).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' }}
+        {{- message.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,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "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",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 40960,
+    "max_window_layers": 36,
+    "model_type": "qwen3",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": 151654,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.7.2",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151654,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

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+    }
+  },
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- set message = messages[index] %}\n    {%- set tool_start = '<tool_response>' %}\n    {%- set tool_start_length = tool_start|length %}\n    {%- set start_of_message = message.content[:tool_start_length] %}\n    {%- set tool_end = '</tool_response>' %}\n    {%- set tool_end_length = tool_end|length %}\n    {%- set start_pos = (message.content|length) - tool_end_length %}\n    {%- if start_pos < 0 %}\n        {%- set start_pos = 0 %}\n    {%- endif %}\n    {%- set end_of_message = message.content[start_pos:] %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+}