初始化项目，由ModelHub XC社区提供模型

Model: trillionlabs/Trillion-7B-preview-GGUF
Source: Original Platform

.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

README.md

@@ -0,0 +1,235 @@
+---
+license: apache-2.0
+tags:
+- finetuned
+- chat
+- easyquant
+- gguf
+- awq
+- easyquant
+- gguf
+language:
+- en
+- ko
+- ja
+- zh
+pipeline_tag: text-generation
+library_name: transformers
+base_model:
+- trillionlabs/Trillion-7B-preview
+---
+
+# Trillion-7B-preview
+
+<p align="center">
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="assets/Signiture_Trillion_White_BG_resized.jpg", width="300", style="margin: 40 auto;">
+  <img src="assets/Signiture_Trillion_Black_BG_resized.jpg" alt="logo", width="300", style="margin: 40 auto;">
+</picture>
+
+
+## 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.
+
+<p align="center">
+<img src="assets/frontier.png" alt="Average Performance vs. Approximate Training FLOPs" width="700">
+</p>
+
+- 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.
+
+
+<details>
+<summary> Full evaluation settings </summary>
+
+| 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.
+
+</details>
+
+### 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

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

special_tokens_map.json

@@ -0,0 +1,153 @@
+{
+  "additional_special_tokens": [
+    "<|reserved_token_1|>",
+    "<|reserved_token_2|>",
+    "<|reserved_token_3|>",
+    "<|reserved_token_4|>",
+    "<|reserved_token_5|>",
+    "<|reserved_token_6|>",
+    "<|reserved_token_7|>",
+    "<|reserved_token_8|>",
+    "<|reserved_token_9|>",
+    "<|reserved_token_10|>",
+    "<|reserved_token_11|>",
+    "<|reserved_token_12|>",
+    "<|reserved_token_13|>",
+    "<|reserved_token_14|>",
+    "<|reserved_token_15|>",
+    "<|reserved_token_16|>",
+    "<|reserved_token_17|>",
+    "<|reserved_token_18|>",
+    "<|reserved_token_19|>",
+    "<|reserved_token_20|>",
+    "<|reserved_token_21|>",
+    "<|reserved_token_22|>",
+    "<|reserved_token_23|>",
+    "<|reserved_token_24|>",
+    "<|reserved_token_25|>",
+    "<|reserved_token_26|>",
+    "<|reserved_token_27|>",
+    "<|reserved_token_28|>",
+    "<|reserved_token_29|>",
+    "<|reserved_token_30|>",
+    "<|reserved_token_31|>",
+    "<|reserved_token_32|>",
+    "<|reserved_token_33|>",
+    "<|reserved_token_34|>",
+    "<|reserved_token_35|>",
+    "<|reserved_token_36|>",
+    "<|reserved_token_37|>",
+    "<|reserved_token_38|>",
+    "<|reserved_token_39|>",
+    "<|reserved_token_40|>",
+    "<|reserved_token_41|>",
+    "<|reserved_token_42|>",
+    "<|reserved_token_43|>",
+    "<|reserved_token_44|>",
+    "<|reserved_token_45|>",
+    "<|reserved_token_46|>",
+    "<|reserved_token_47|>",
+    "<|reserved_token_48|>",
+    "<|reserved_token_49|>",
+    "<|reserved_token_50|>",
+    "<|reserved_token_51|>",
+    "<|reserved_token_52|>",
+    "<|reserved_token_53|>",
+    "<|reserved_token_54|>",
+    "<|reserved_token_55|>",
+    "<|reserved_token_56|>",
+    "<|reserved_token_57|>",
+    "<|reserved_token_58|>",
+    "<|reserved_token_59|>",
+    "<|reserved_token_60|>",
+    "<|reserved_token_61|>",
+    "<|reserved_token_62|>",
+    "<|reserved_token_63|>",
+    "<|reserved_token_64|>",
+    "<|reserved_token_65|>",
+    "<|reserved_token_66|>",
+    "<|reserved_token_67|>",
+    "<|reserved_token_68|>",
+    "<|reserved_token_69|>",
+    "<|reserved_token_70|>",
+    "<|reserved_token_71|>",
+    "<|reserved_token_72|>",
+    "<|reserved_token_73|>",
+    "<|reserved_token_74|>",
+    "<|reserved_token_75|>",
+    "<|reserved_token_76|>",
+    "<|reserved_token_77|>",
+    "<|reserved_token_78|>",
+    "<|reserved_token_79|>",
+    "<|reserved_token_80|>",
+    "<|reserved_token_81|>",
+    "<|reserved_token_82|>",
+    "<|reserved_token_83|>",
+    "<|reserved_token_84|>",
+    "<|reserved_token_85|>",
+    "<|reserved_token_86|>",
+    "<|reserved_token_87|>",
+    "<|reserved_token_88|>",
+    "<|reserved_token_89|>",
+    "<|reserved_token_90|>",
+    "<|reserved_token_91|>",
+    "<|reserved_token_92|>",
+    "<|reserved_token_93|>",
+    "<|reserved_token_94|>",
+    "<|reserved_token_95|>",
+    "<|reserved_token_96|>",
+    "<|reserved_token_97|>",
+    "<|reserved_token_98|>",
+    "<|reserved_token_99|>",
+    "<|reserved_token_100|>",
+    "<|reserved_token_101|>",
+    "<|reserved_token_102|>",
+    "<|reserved_token_103|>",
+    "<|reserved_token_104|>",
+    "<|reserved_token_105|>",
+    "<|reserved_token_106|>",
+    "<|reserved_token_107|>",
+    "<|reserved_token_108|>",
+    "<|reserved_token_109|>",
+    "<|reserved_token_110|>",
+    "<|reserved_token_111|>",
+    "<|reserved_token_112|>",
+    "<|reserved_token_113|>",
+    "<|reserved_token_114|>",
+    "<|reserved_token_115|>",
+    "<|reserved_token_116|>",
+    "<|reserved_token_117|>",
+    "<|reserved_token_118|>",
+    "<|reserved_token_119|>",
+    "<|reserved_token_120|>",
+    "<|reserved_token_121|>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

trillion-7b-preview.bf16.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c200924c4022dd55f60b4751c35964d3939613a13eec1ecc4ef465d744d25ba0
+size 15057589568

trillion-7b-preview.q2_k.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a469f03ed8b9fab5b1de5731b8b27f111890f2b6a47561bf71e69422c20c25a
+size 2989573440

trillion-7b-preview.q3_k_m.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2dd3544d888ab0943eb3732274d13c32ed30bb1345c81acac8c3d3b268db329
+size 3794703680

trillion-7b-preview.q4_k_m.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8fab3c11c17c8d4408fdeddcf746594aaf0a34c42b956d5b82502b15b3df90b
+size 4629996864

trillion-7b-preview.q5_k_m.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8aaee72b389047417ea72d974598e9f94e53418880d7ff2c710831d0ec8999fc
+size 5381367104

trillion-7b-preview.q6_k.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16d27b208957887c530ae1b48e88e3687fd9a86bb7a50b1438b19601abd13103
+size 6179697984

trillion-7b-preview.q8_0.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:422160246f0d57919bf112ab38b310e9a60932f97521d21922e07557aa2a94fe
+size 8002311488