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.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

@@ -1,3 +1,222 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qihoo360/Light-MT-7B/blob/main/LICENSE
+language:
+- en
+- zh
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-7B
+tags:
+- machine-translation
+- multilingual
+- qwen2
+library_name: transformers
+---
+
+# Light-MT-7B
+<a href="https://huggingface.co/qihoo360/Light-MT-7B" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FF6B6B" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Introduction
+
+Light-MT-7B is a machine translation focused variant of Qwen2.5-7B developed by 360 AI Research. The model follows the Multilingual Translation Policy Optimization (MtPO) pipeline introduced in the paper "Extending Foundation Models to Low-Resource Languages" and targets Southeast Asian and other under-served languages while preserving general instruction-following ability.
+
+**This repo contains the machine translation specialized 7B model**, which has the following features:
+- Type: Causal Language Models for Machine Translation
+- Training Stage: Continued pretraining, curriculum SFT, and MtPO reinforcement learning
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- Number of Parameters: 7.61B (6.53B non-embedding)
+- Number of Layers: 28
+- Number of Attention Heads (GQA): 28 for Q and 4 for KV
+- Context Length: Up to 131,072 tokens
+- Vocabulary Size: 180,736 tokens with MtPO vocabulary expansion
+
+## Model Highlights
+
+Key outcomes from the MtPO recipe:
+
+- 2.1x-5.4x compression gains on FLORES-Plus corpora across Khmer, Lao, Myanmar, Thai, Tibetan, and other scripts through targeted tokenizer expansion.
+- Curriculum supervised fine-tuning over a 7M-sample mixture progressing from general instructions to ASEAN-focused translation prompts.
+- MtPO reinforcement learning that maintains entropy during decoding via asymmetric clipping, temperature consistency, and microbatch-normalized advantages.
+- Reinforcement Learning with Verifiable Rewards (RLVR) to enforce length ratios, structural tokens, language targeting, and code mixing checks for reliable outputs.
+- 200B continued pretraining tokens plus 60k MtPO steps, preserving BBH, CMMLU, HellaSwag, and MMLU performance while lifting translation quality.
+
+## Requirements
+
+The code of Light-MT-7B is compatible with the latest Hugging Face `transformers` library. We recommend using the latest version of `transformers`.
+
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
+```
+
+## Quickstart
+
+Here provides a code snippet to show you how to load the tokenizer and model for machine translation tasks.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "qihoo360/Light-MT-7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example translation prompt
+prompt = "Translate the following English text to Chinese: Hello, how are you today?"
+messages = [
+    {"role": "system", "content": "You are a professional translator. Translate the given text accurately and naturally."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    do_sample=True
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## Training Pipeline (MtPO)
+
+MtPO runs in four stages from tokenizer expansion to reinforcement learning alignment.
+
+- **Stage 1 - Vocabulary expansion:** Extend the Qwen2.5 tokenizer with 3k-4k tokens per target language (Khmer, Lao, Mongolian, Myanmar, Tamil, Thai, Tibetan, Uyghur). FLORES-Plus diagnostics show 2.1x-5.4x compression gains, cutting Khmer token counts from 402 to 103 for representative passages.
+- **Stage 2 - Balanced continued pretraining:** Continue training on 200B tokens with a 1:1 mix between English and the expanded low-resource corpus to preserve high-resource coverage while materially improving low-resource fluency.
+- **Stage 3 - Curriculum SFT:** Train on a 7M-sample blend (5:1 general instructions vs. multilingual data) that progresses from base instruction-following to ASEAN translation and mixed-format prompts.
+- **Stage 4 - MtPO reinforcement learning:** Optimize with entropy-tempered policy updates that keep sampling temperature consistent, apply asymmetric ratio clipping, and normalize advantages at the microbatch level to avoid length bias or entropy collapse.
+
+## Verifiable Reward Guardrails
+
+Reinforcement Learning with Verifiable Rewards (RLVR) combines the translation reward model with deterministic validators. During MtPO we sample K candidates per prompt, score them with RLVR, and keep the top-G diverse outputs for gradient updates. Each candidate is checked for:
+- Length ratio safety relative to the source (default bounds 0.5-2.0 with soft penalties outside range)
+- Structural token preservation for HTML, Markdown, and code blocks using lightweight parsers
+- Target-language verification via a confidence-gated language ID classifier
+- Code-mixing penalties that suppress unintended language drift
+
+These verifiable rewards are added to the semantic score so bad outputs receive immediate negative credit, while high-quality candidates remain eligible for optimization.
+
+## Data and Training Budget
+
+Summary of resources and evaluation suites used during MtPO development.
+
+- Continued pretraining: 200B tokens with adaptive sampling over English, ASEAN, Tibetan, Mongolian, Tamil, and Uyghur corpora
+- MtPO reinforcement learning: 60k steps, batch size 128, top-G candidate selection with RLVR filtering
+- Reward model: Preference data spans ten error categories (accuracy, fluency, terminology, formatting, code-mixing, etc.)
+- Benchmarks: FLORES-Plus (90 directions), BBH, CMMLU, HellaSwag, MMLU
+
+## Model Details
+
+- **Model Type**: Qwen2-based Causal Language Model
+- **Language(s)**: Multilingual (English, Chinese, Khmer, Lao, Myanmar, Thai, Tibetan, Mongolian, Tamil, Malay, Indonesian, Filipino, Vietnamese, Uyghur, etc.)
+- **License**: Apache 2.0
+- **Finetuned from**: Qwen/Qwen2.5-7B
+- **Model Size**: 7.61B parameters
+- **Context Length**: 131,072 tokens
+
+## Usage
+
+This model is specifically designed for machine translation tasks. It can handle various translation scenarios including:
+
+- English <-> Chinese translation
+- Multilingual translation tasks
+- Professional document translation
+- Conversational translation
+
+## Evaluation
+
+### Translation and General Benchmarks
+
+Light-MT-7B-MtPO is evaluated on FLORES-Plus (90 directions) and standard instruction-following benchmarks. Scores below use sacreBLEU (higher is better) and zero-shot accuracy (percentage).
+
+| Model | Group | xx->en | en->xx | xx->xx | Avg. | BBH | CMMLU | HellaSwag | MMLU |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Gemma3-27B-IT | Multilingual chat | **36.8** | 30.7 | 22.3 | 24.7 | 55.9 | 55.9 | 55.9 | **56.0** |
+| Qwen3-8B | Multilingual chat | 31.1 | 23.3 | 14.4 | 16.9 | **63.8** | 60.8 | 26.0 | 51.3 |
+| Qwen2.5-7B-Instruct | Multilingual chat | 24.8 | 17.4 | 9.2 | 11.6 | 54.4 | **64.1** | **85.2** | 40.9 |
+| Apertus-8B-Instruct | Multilingual chat | 32.5 | 25.7 | 15.6 | 18.3 | 49.2 | 45.3 | 64.2 | 45.2 |
+| Tower-Plus-9B | Multilingual chat | 28.2 | 18.3 | 9.8 | 12.5 | 40.4 | 57.2 | 73.1 | 42.1 |
+| Qwen-MT-Plus | Translation-focused | 34.0 | 29.6 | 19.6 | 22.1 | - | - | - | - |
+| Seed-X-PPO-7B | Translation-focused | 25.9 | 22.6 | 10.5 | 13.3 | - | - | - | - |
+| Hunyuan-MT-7B | Translation-focused | 24.6 | 23.4 | 14.8 | 16.6 | - | - | - | - |
+| Light-TLLM-7B-SFT | Our models | 35.4 | 32.0 | 22.7 | 24.3 | 59.6 | 61.4 | 83.7 | 47.2 |
+| **Light-TLLM-7B-MtPO** | Our models | 36.1 | **32.7** | **23.1** | **24.9** | 60.9 | 63.2 | **85.2** | 48.5 |
+
+- en->xx directions gain +1.1 BLEU over the next best 7B system while preserving reasoning accuracy (+1.3 MMLU over SFT).
+- Average BLEU across all FLORES-Plus directions rises to 24.9 despite the compact 7B footprint.
+
+### Tokenizer Efficiency
+
+Vocabulary expansion provides substantial compression on targeted scripts (higher compression ratio means fewer tokens per sentence).
+
+| Language | Added tokens | Old compression ratio | New compression ratio | Speedup |
+| --- | --- | --- | --- | --- |
+| Khmer | 3712 | 0.85 | 3.49 | 4.09x |
+| Lao | 3359 | 0.85 | 3.05 | 3.59x |
+| Myanmar | 3226 | 0.69 | 2.87 | 4.17x |
+| Thai | 2958 | 1.79 | 2.97 | 1.66x |
+| Tibetan | 3920 | 0.75 | 4.03 | 5.39x |
+
+- Khmer passages shrink from 402 tokens to 103 tokens in the running example used in the paper.
+- Compression gains translate into lower latency and memory cost during decoding for low-resource scripts.
+
+### Constraint Reliability (RLVR)
+
+RLVR introduces deterministic checks that reduce failure modes compared with general chat models and MT baselines.
+
+| Model | Language targeting | Length control | Format preservation | Code mixing | Overall |
+| --- | --- | --- | --- | --- | --- |
+| **Light-TLLM-7B-MtPO** | **97.8** | 99.2 | **92.15** | 92.3 | **95.3** |
+| Qwen2.5-7B-Instruct | 92.0 | 97.0 | 51.8 | 62.8 | 75.9 |
+| Gemma3-27B-IT | 97.4 | 91.6 | 42.1 | 90.9 | 80.5 |
+| Qwen-MT-Plus | 97.6 | **99.8** | 82.5 | 94.8 | 93.6 |
+| Seed-X-PPO-7B | 97.6 | 79.8 | 79.0 | 90.3 | 86.6 |
+| DeepSeek-V3 | 95.4 | 95.7 | 67.6 | 95.0 | 88.4 |
+| Hunyuan-MT-7B | 91.8 | 90.7 | 71.1 | **96.2** | 87.4 |
+
+- Format retention jumps to 92.15 percent versus 51.8 percent for Qwen2.5-7B-Instruct, mitigating HTML or Markdown corruption.
+- Language targeting stays above 97 percent while MtPO avoids verbosity by normalizing advantages at the microbatch level.
+- Overall pass rate reaches 95.3 percent, surpassing Qwen2.5-7B-Instruct by 19.4 points, DeepSeek-V3 by 6.9 points, and Qwen-MT-Plus by 1.7 points despite identical constraint settings.
+
+### Per-Language FLORES Highlights
+
+- **English->Thai:** 34.1 BLEU, +1.5 over Qwen-MT-Plus.
+- **English->Myanmar:** 12.9 BLEU with stable length control.
+- **English->Filipino:** 35.4 BLEU after MtPO, combining instruction fidelity and translation quality.
+- **Khmer->English:** 44.7 BLEU, reflecting gains from tokenizer expansion.
+- **Vietnamese->English:** 37.6 BLEU with consistent improvements across ASEAN language pairs.
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@inproceedings{liu2026mtpo,
+    title = {Light-MT-7B},
+    author = {Light-MT Team},
+    booktitle = {International Conference on Learning Representations},
+    year = {2025},
+    url = {https://huggingface.co/qihoo360/Light-MT-7B}
+}
+```
+
+## Disclaimer
+
+This model is provided for research and educational purposes. Please ensure responsible use and compliance with applicable laws and regulations when using this model.

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\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>" }}
+    {%- 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) 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' }}
+{%- endif %}

config.json

@@ -0,0 +1,59 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "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": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 180736
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.55.2"
+}

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