commit d21f84761cca0eb03b23bf6e777517251ac6a52c Author: ModelHub XC Date: Wed Sep 2 13:08:12 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: trillionlabs/Trillion-7B-preview-AWQ Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm 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 \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..0a9f57a --- /dev/null +++ b/README.md @@ -0,0 +1,235 @@ +--- +license: apache-2.0 +tags: +- finetuned +- chat +- easyquant +- gguf +- awq +- easyquant +- awq +language: +- en +- ko +- ja +- zh +pipeline_tag: text-generation +library_name: transformers +base_model: +- trillionlabs/Trillion-7B-preview +--- + +# Trillion-7B-preview + +

+ + + logo + + + +## Introduction + +We introduce Trillion-7B-preview, a preview of our latest large language model designed to push the boundaries of multilingual scalability and performance. + + +When comparing performance to training FLOPs for Trillion-7B-preview with competitive models, our model pushes the Pareto frontier, achieving around 66.5% average performance while using significantly fewer compute (~9.3×10²² FLOPs). It outperforms models like Mistral-7B-Instruct-v0.3 and SOLAR-10.7B-Instruct-v1.0 while remaining competitive with models requiring 3-8× more compute such as Qwen2.5-7B-Instruct and EXAONE-3.5-7.8B-Instruct. For full benchmark results, see tables below. + +

+Average Performance vs. Approximate Training FLOPs +

+ +- Type: Causal Language Model +- Training Stage: Pre-training & Post-training +- Architecture: Transformer Decoder with RoPE, SwiGLU, RMSNorm +- Number of Parameters: 7.76B +- Number of Layers: 32 +- Number of Attention Heads: 32 +- Context Length: 4,096 +- Number of Tokens seen: 2T +- Vocab Size: 128,128 + + +## Quickstart + +Here is a code snippet with `apply_chat_template` that demonstrates how to load the tokenizer and model and generate text. + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "trillionlabs/Trillion-7B-preview" + +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.bfloat16, + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +prompt = "Tell me a hilarious knock knock joke." +messages = [ + {"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["input_ids"], + attention_mask=model_inputs["attention_mask"], + max_new_tokens=512 +) +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) + +""" +Sure! Here's a classic knock-knock joke that's guaranteed to make you chuckle: +Knock, knock. +Who's there? +Lettuce. +Lettuce who? +Lettuce in, it's too cold out here! +""" +``` + +## Evaluation + +We select a wide variety of benchmarks that evaluate general reasoning, knowledge recall, coding abilities, mathematical reasoning, and instruction following capabilities. We evaluated Trillion-7B-preview along with several leading large language models of similar size. Our model especially demonstrates strong performance on Korean benchmarks. + + +
+ Full evaluation settings + +| Benchmark | Language | Evaluation Setting | Metric | +|:----------|:---------|:------------------|:-------| +| **General Reasoning and Reading Comprehension** | | | | +| • HellaSwag | English | 0-shot | accuracy | +| • TruthfulQA_mc1 | English | 6-shot | accuracy | +| • TruthfulQA_mc2 | English | 6-shot | accuracy | +| • ARC:C | English | 0-shot | accuracy | +| • HAERAE | Korean | 3-shot | accuracy | +| • KoBEST | Korean | 5-shot | accuracy | +| • BBH | English | 0-shot, CoT | accuracy | +| • xwinograd_en | English | 0-shot | accuracy | +| • xwinograd_jp | Japanese | 0-shot | accuracy | +| • xwinograd_zh | Chinese | 0-shot | accuracy | +| **Knowledge Recall** | | | | +| • KMMLU | Korean | 5-shot | accuracy | +| • MMLU | English | 5-shot | accuracy | +| • Global-MMLU-Lite-en | English | 5-shot | accuracy | +| • Global-MMLU-Lite-ko | Korean | 5-shot | accuracy | +| • Global-MMLU-Lite-ja | Japanese | 5-shot | accuracy | +| • Global-MMLU-Lite-zh | Chinese | 5-shot | accuracy | +| **Coding** | | | | +| • HumanEval | English | 0-shot, CoT | pass@1 | +| • MBPP | English | 0-shot, CoT| pass@1 | +| **Mathematical Reasoning** | | | | +| • GSM8k | English | 0-shot, CoT | exact-match | +| • MATH | English | 0-shot, CoT | exact-match | +| • GPQA | English | 4-shot | accuracy | +| • HRM8k | Korean | 0-shot, CoT | exact-match | +| **Instruction Following and Chat** | | | | +| • IFEval | English | 0-shot | strict-average | +| • koIFEval* | Korean | 0-shot | strict-average | +| • MT-Bench** | English | LLM-as-a-judge (gpt-4o-2024-08-06) | LLM score | +| • KO-MT-Bench** | Korean | LLM-as-a-judge (gpt-4o-2024-08-06) | LLM score | +| • LogicKor** | Korean | LLM-as-a-judge (gpt-4o-2024-08-06) | LLM score | + +- *Note that koIFEval is our in-house evaluation benchmark for assessing instruction-following capabilities in Korean. +- **Note that MT-Bench, KO-MT-Bench, and LogicKor use a 10-point scale. + +
+ +### Benchmark Results + +- Trillion-7B-preview +- [LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct) +- [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) +- [meta-llama/Llama-3.1-8B-Instruct](meta-llama/Llama-3.1-8B-Instruct) +- [Qwen/Qwen2.5-7B-Instruct](Qwen/Qwen2.5-7B-Instruct) +- [upstage/SOLAR-10.7B-Instruct-v1.0](upstage/SOLAR-10.7B-Instruct-v1.0) +- [mistralai/Mistral-7B-Instruct-v0.3](mistralai/Mistral-7B-Instruct-v0.3) + + +### General Reasoning and Factuality + +| Benchmark | Trillion-7B-preview | EXAONE-3.5-7.8B-Instruct | gemma-2-9b-it | Llama-3.1-8B-Instruct | Qwen2.5-7B-Instruct | SOLAR-10.7B-Instruct-v1.0 | Mistral-7B-Instruct-v0.3 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| HellaSwag | 58.94 | 60.04 | 59.72 | 59.81 | 61.97 | 68.72 | 65.79 | +| TruthfulQA_mc1 | 36.10 | 40.64 | 42.96 | 38.07 | 47.74 | 56.18 | 42.47 | +| TruthfulQA_mc2 | 54.10 | 59.74 | 60.09 | 54.54 | 64.72 | 70.64 | 59.41 | +| ARC:C | 54.44 | 56.40 | 62.97 | 53.58 | 52.99 | 60.07 | 58.11 | +| HAERAE | 80.02 | 76.08 | 68.01 | 63.15 | 65.17 | 60.86 | 47.75 | +| KoBEST | 79.61 | 78.57 | 79.98 | 70.09 | 79.24 | 75.20 | 66.50 | +| KMMLU | 48.09 | 45.39 | 46.66 | 41.41 | 50.15 | 41.66 | 33.59 | +| MMLU | 63.52 | 65.65 | 72.24 | 68.32 | 74.23 | 65.20 | 61.84 | +| Global-MMLU-Lite-en | 67.75 | 69.50 | 76.25 | 67.50 | 77.25 | 71.75 | 65.50 | +| Global-MMLU-Lite-ko | 60.75 | 60.00 | 64.25 | 54.00 | 59.25 | 53.75 | 43.00 | +| Global-MMLU-Lite-ja | 60.75 | 45.75 | 66.50 | 54.50 | 65.75 | 50.75 | 50.00 | +| Global-MMLU-Lite-zh | 59.50 | 50.00 | 63.75 | 60.25 | 68.75 | 57.00 | 47.25 | +| BBH | 41.94 | 53.30 | 28.77 | 43.16 | 53.68 | 52.91 | 45.09 | +| xwinograd_en | 87.78 | 87.10 | 89.55 | 88.09 | 85.63 | 87.35 | 88.39 | +| xwinograd_jp | 79.98 | 74.45 | 80.92 | 76.02 | 72.89 | 72.58 | 70.70 | +| xwinograd_zh | 73.81 | 69.44 | 68.06 | 76.19 | 81.55 | 74.60 | 71.83 | + +### Coding + +| Benchmark | Trillion-7B-preview | EXAONE-3.5-7.8B-Instruct | gemma-2-9b-it | Llama-3.1-8B-Instruct | Qwen2.5-7B-Instruct | SOLAR-10.7B-Instruct-v1.0 | Mistral-7B-Instruct-v0.3 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| HumanEval | 55.48 | 79.26 | 60.98 | 67.68 | 81.71 | 34.76 | 36.59 | +| MBPP | 40.40 | 61.40 | 8.40 | 39.20 | 51.00 | 29.40 | 36.00 | + +### Mathematical Reasoning + +| Benchmark | Trillion-7B-preview | EXAONE-3.5-7.8B-Instruct | gemma-2-9b-it | Llama-3.1-8B-Instruct | Qwen2.5-7B-Instruct | SOLAR-10.7B-Instruct-v1.0 | Mistral-7B-Instruct-v0.3 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| GSM8k | 72.25 | 87.79 | 73.69 | 74.98 | 88.86 | 62.93 | 35.94 | +| MATH | 32.70 | 70.68 | - | 38.30 | 71.50 | 14.38 | 12.12 | +| GPQA | 32.81 | 38.61 | 36.83 | 30.58 | 34.15 | 28.35 | 32.59 | +| HRM8k | 30.10 | 38.99 | 16.04 | - | 41.51 | 20.68 | 7.89 | + +### Instruction Following and Chat + +| Benchmark | Trillion-7B-preview | EXAONE-3.5-7.8B-Instruct | gemma-2-9b-it | Llama-3.1-8B-Instruct | Qwen2.5-7B-Instruct | SOLAR-10.7B-Instruct-v1.0 | Mistral-7B-Instruct-v0.3 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| IFEval | 79.13 | 81.42 | 75.48 | 74.93 | 75.85 | 51.61 | 52.64 | +| koIFEval | 66.58 | 54.65 | 43.30 | 36.07 | 48.55 | 26.12 | 34.22 | +| MT-Bench | 7.00 | 8.15 | 7.81 | 6.32 | 7.86 | 6.76 | 6.84 | +| KO-MT-Bench | 6.27 | 8.13 | 7.01 | 4.27 | 6.31 | 2.89 | 4.07 | +| LogicKor | 8.14 | 9.25 | 8.33 | 6.45 | 7.99 | 1.85 | 4.76 + + + + +## Limitations + +- Language Support: The model is optimized for English, Korean, Japanese, and Chinese. Usage with other languages may result in degraded performance. +- Knowledge Cutoff: The model's information is limited to data available up to August 2023. +- Safety Mechanisms: This release does not yet include comprehensive safety features. Future updates will address this area. +- Release Status: This is a preliminary release version with planned enhancements and updates forthcoming. + + +## License +This model repository is licensed under the Apache-2.0 License. + + +## Citation +``` +@article{trillion7Bpreview, + title={Trillion-7B-preview}, + author={trillionlabs}, + year={2025}, + url={https://huggingface.co/trillionlabs/Trillion-7B-preview} +} +``` + +## Contact +For inquiries, please contact: info@trillionlabs.co \ No newline at end of file diff --git a/assets/Signiture_Trillion_Black_BG_resized.jpg b/assets/Signiture_Trillion_Black_BG_resized.jpg new file mode 100644 index 0000000..d7e7488 Binary files /dev/null and b/assets/Signiture_Trillion_Black_BG_resized.jpg differ diff --git a/assets/Signiture_Trillion_White_BG_resized.jpg b/assets/Signiture_Trillion_White_BG_resized.jpg new file mode 100644 index 0000000..46ecd23 Binary files /dev/null and b/assets/Signiture_Trillion_White_BG_resized.jpg differ diff --git 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