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Model: 360zhinao/Light-TLLM-7B Source: Original Platform
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---
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license: apache-2.0
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license_link: https://huggingface.co/Qihoo360/Light-TLLM-7B/blob/main/LICENSE
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language:
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- en
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- zh
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-7B
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tags:
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- machine-translation
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- multilingual
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- qwen2
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library_name: transformers
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---
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# Light-TLLM-7B
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<a href="https://huggingface.co/qihoo360/Light-TLLM-7B" target="_blank" style="margin: 2px;">
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<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FF6B6B" style="display: inline-block; vertical-align: middle;"/>
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</a>
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## Introduction
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Light-TLLM-7B is a machine translation focused variant of Qwen2.5-7B developed by 360 AI Research.
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**This repo contains the machine translation specialized 7B model**, which has the following features:
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- Type: Causal Language Models for Machine Translation
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- Training Stage: Continued pretraining, curriculum SFT, and MtPO reinforcement learning
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- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
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- Number of Parameters: 7.61B (6.53B non-embedding)
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- Number of Layers: 28
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- Number of Attention Heads (GQA): 28 for Q and 4 for KV
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- Context Length: Up to 131,072 tokens
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- Vocabulary Size: 180,736 tokens with MtPO vocabulary expansion
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## Requirements
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The code of Light-TLLM-7B is compatible with the latest Hugging Face `transformers` library. We recommend using the latest version of `transformers`.
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With `transformers<4.37.0`, you will encounter the following error:
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```
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KeyError: 'qwen2'
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```
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## Quickstart
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Here provides a code snippet to show you how to load the tokenizer and model for machine translation tasks.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "qihoo360/Light-TLLM-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Example translation prompt
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prompt = "Translate the following English text to Chinese: Hello, how are you today?"
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messages = [
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{"role": "system", "content": "You are a professional translator. Translate the given text accurately and naturally."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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temperature=0.7,
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do_sample=True
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## Training Pipeline (MtPO)
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Runs in four stages from tokenizer expansion to reinforcement learning alignment.
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- **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.
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- **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.
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- **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.
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- **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.
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## Verifiable Reward Guardrails
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Reinforcement Learning with Verifiable Rewards (RLVR) combines the translation reward model with deterministic validators. During RL 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:
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- Length ratio safety relative to the source (default bounds 0.5-2.0 with soft penalties outside range)
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- Structural token preservation for HTML, Markdown, and code blocks using lightweight parsers
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- Target-language verification via a confidence-gated language ID classifier
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- Code-mixing penalties that suppress unintended language drift
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These verifiable rewards are added to the semantic score so bad outputs receive immediate negative credit, while high-quality candidates remain eligible for optimization.
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## Data and Training Budget
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Summary of resources and evaluation suites used during MtPO development.
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- Continued pretraining: 200B tokens with adaptive sampling over English, ASEAN, Tibetan, Mongolian, Tamil, and Uyghur corpora
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- Reinforcement learning: 60k steps, batch size 128, top-G candidate selection with RLVR filtering
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- Reward model: Preference data spans ten error categories (accuracy, fluency, terminology, formatting, code-mixing, etc.)
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- Benchmarks: FLORES-Plus (90 directions), BBH, CMMLU, HellaSwag, MMLU
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## Model Details
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- **Model Type**: Qwen2-based Causal Language Model
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- **Language(s)**: Multilingual (English, Chinese, Khmer, Lao, Myanmar, Thai, Tibetan, Mongolian, Tamil, Malay, Indonesian, Filipino, Vietnamese, Uyghur, etc.)
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- **License**: Apache 2.0
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- **Finetuned from**: Qwen/Qwen2.5-7B
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- **Model Size**: 7.61B parameters
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- **Context Length**: 131,072 tokens
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## Usage
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This model is specifically designed for machine translation tasks. It can handle various translation scenarios including:
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- English <-> Chinese translation
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- Multilingual translation tasks
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- Professional document translation
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- Conversational translation
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## Evaluation
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### Translation and General Benchmarks
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Light-TLLM-7B is evaluated on FLORES-Plus (90 directions) and standard instruction-following benchmarks. Scores below use sacreBLEU (higher is better) and zero-shot accuracy (percentage).
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| Model | Group | xx->en | en->xx | xx->xx | Avg. | BBH | CMMLU | HellaSwag | MMLU |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| Gemma3-27B-IT | Multilingual chat | **36.8** | 30.7 | 22.3 | 24.7 | 55.9 | 55.9 | 55.9 | **56.0** |
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| Qwen3-8B | Multilingual chat | 31.1 | 23.3 | 14.4 | 16.9 | **63.8** | 60.8 | 26.0 | 51.3 |
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| Qwen2.5-7B-Instruct | Multilingual chat | 24.8 | 17.4 | 9.2 | 11.6 | 54.4 | **64.1** | **85.2** | 40.9 |
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| Apertus-8B-Instruct | Multilingual chat | 32.5 | 25.7 | 15.6 | 18.3 | 49.2 | 45.3 | 64.2 | 45.2 |
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| Tower-Plus-9B | Multilingual chat | 28.2 | 18.3 | 9.8 | 12.5 | 40.4 | 57.2 | 73.1 | 42.1 |
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| Qwen-MT-Plus | Translation-focused | 34.0 | 29.6 | 19.6 | 22.1 | - | - | - | - |
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| Seed-X-PPO-7B | Translation-focused | 25.9 | 22.6 | 10.5 | 13.3 | - | - | - | - |
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| Hunyuan-MT-7B | Translation-focused | 24.6 | 23.4 | 14.8 | 16.6 | - | - | - | - |
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| Light-TLLM-7B-SFT | Our models | 35.4 | 32.0 | 22.7 | 24.3 | 59.6 | 61.4 | 83.7 | 47.2 |
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| **Light-TLLM-7B-RL** | Our models | 36.1 | **32.7** | **23.1** | **24.9** | 60.9 | 63.2 | **85.2** | 48.5 |
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- en->xx directions gain +1.1 BLEU over the next best 7B system while preserving reasoning accuracy (+1.3 MMLU over SFT).
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- Average BLEU across all FLORES-Plus directions rises to 24.9 despite the compact 7B footprint.
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### Tokenizer Efficiency
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Vocabulary expansion provides substantial compression on targeted scripts (higher compression ratio means fewer tokens per sentence).
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| Language | Added tokens | Old compression ratio | New compression ratio | Speedup |
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| --- | --- | --- | --- | --- |
|
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| Khmer | 3712 | 0.85 | 3.49 | 4.09x |
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| Lao | 3359 | 0.85 | 3.05 | 3.59x |
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| Myanmar | 3226 | 0.69 | 2.87 | 4.17x |
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| Thai | 2958 | 1.79 | 2.97 | 1.66x |
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| Tibetan | 3920 | 0.75 | 4.03 | 5.39x |
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- Khmer passages shrink from 402 tokens to 103 tokens in the running example used in the paper.
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- Compression gains translate into lower latency and memory cost during decoding for low-resource scripts.
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### Constraint Reliability (RLVR)
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RLVR introduces deterministic checks that reduce failure modes compared with general chat models and MT baselines.
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| Model | Language targeting | Length control | Format preservation | Code mixing | Overall |
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| --- | --- | --- | --- | --- | --- |
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| **Light-TLLM-7B-RL** | **97.8** | 99.2 | **92.15** | 92.3 | **95.3** |
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| Qwen2.5-7B-Instruct | 92.0 | 97.0 | 51.8 | 62.8 | 75.9 |
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| Gemma3-27B-IT | 97.4 | 91.6 | 42.1 | 90.9 | 80.5 |
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| Qwen-MT-Plus | 97.6 | **99.8** | 82.5 | 94.8 | 93.6 |
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| Seed-X-PPO-7B | 97.6 | 79.8 | 79.0 | 90.3 | 86.6 |
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| DeepSeek-V3 | 95.4 | 95.7 | 67.6 | 95.0 | 88.4 |
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| Hunyuan-MT-7B | 91.8 | 90.7 | 71.1 | **96.2** | 87.4 |
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- Format retention jumps to 92.15 percent versus 51.8 percent for Qwen2.5-7B-Instruct, mitigating HTML or Markdown corruption.
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- Language targeting stays above 97 percent while MtPO avoids verbosity by normalizing advantages at the microbatch level.
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- 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.
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### Per-Language FLORES Highlights
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- **English->Thai:** 34.1 BLEU, +1.5 over Qwen-MT-Plus.
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- **English->Myanmar:** 12.9 BLEU with stable length control.
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- **English->Filipino:** 35.4 BLEU after MtPO, combining instruction fidelity and translation quality.
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- **Khmer->English:** 44.7 BLEU, reflecting gains from tokenizer expansion.
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- **Vietnamese->English:** 37.6 BLEU with consistent improvements across ASEAN language pairs.
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```
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@inproceedings{liu2026mtpo,
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title = {Light-TLLM-7B},
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author = {Light-MT Team},
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booktitle = {International Conference on Learning Representations},
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year = {2025},
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url = {https://huggingface.co/qihoo360/Light-TLLM-7B}
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}
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```
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## Disclaimer
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This model is provided for research and educational purposes. Please ensure responsible use and compliance with applicable laws and regulations when using this model.
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added_tokens.json
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
|
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{{- "\n" }}
|
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{{- tool | tojson }}
|
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{%- endfor %}
|
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{{- "\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" }}
|
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{%- else %}
|
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
|
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{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
|
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{%- endif %}
|
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{%- endif %}
|
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{%- 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' }}
|
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{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
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{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
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{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
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{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
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{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
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{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
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{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
|
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"architectures": [
|
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"Qwen2ForCausalLM"
|
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"eos_token_id": 151643,
|
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"hidden_act": "silu",
|
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"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",
|
||||
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31
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31
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Normal file
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3
tokenizer.json
Normal file
3
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3
tokenizer_config.json
Normal file
3
tokenizer_config.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cdfce3bdb99d99dcd107818abf88d1384ac6f3296e01e18745773f13607b2fb5
|
||||
size 5256977
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user