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Model: flux-inc/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 Source: Original Platform
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51
LICENSE
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LICENSE
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Qwen LICENSE AGREEMENT
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||||
|
||||
Qwen LICENSE AGREEMENT Release Date: September 19, 2024
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||||
|
||||
By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
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1. Definitions
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a. This Qwen LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
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b. "We" (or "Us") shall mean Alibaba Cloud.
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c. "You" (or "Your") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.
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d. "Third Parties" shall mean individuals or legal entities that are not under common control with us or you.
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e. "Qwen" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by us.
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You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Alibaba Cloud's intellectual property or other rights owned by us embodied in the Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Materials.
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a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.
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b. If you use the Materials or any outputs or results therefrom to create, train, fine-tune, or improve an AI model that is distributed or made available, you shall prominently display “Built with Qwen” or “Improved using Qwen” in the related product documentation.
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6. Intellectual Property
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a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
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a. We are not obligated to support, update, provide training for, or develop any further version of the Qwen Materials or to grant any license thereto.
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8. Survival and Termination.
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a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
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b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 7 and 9 shall survive the termination of this Agreement.
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9. Governing Law and Jurisdiction.
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a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
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b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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110
README-ja.md
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README-ja.md
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---
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE
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language:
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- en
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||||
pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-32B
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tags:
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- chat
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library_name: transformers
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---
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||||
# Flux-Japanese-Qwen2.5-32B-Instruct-V1.0
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[[English](./README.md)] [**Japanese**]
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Flux-Japanese-Qwen2.5-32B-Instruct-V1.0は、320億個のパラメータを持つオープンウェイトの大規模言語モデルです。日本語に関する深い知識と、高度な推論能力および言語能力を特長としています。Qwen2.5‑32B‑Instruct をベースとしてトレーニングされ、Apache 2.0 オープンソースライセンスの下で提供されています。
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# 🏆 Open-Japanese-LLM-Leaderboard 第1位
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||||
[Open LLM Japanese LLM Leaderboard](https://huggingface.co/spaces/deep-analysis-research/open-japanese-llm-leaderboard) において、以下の結果となりました。
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- ベースモデル：「Qwen2.5‑32B‑Instruct 」（平均スコア：0.6553）
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- 元評価トップのモデル：「D2IL‑Japanese‑Qwen2.5‑32B‑Instruct‑v0.1 」（平均スコア： 0.7100）
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- 本モデル：「Flux‑Japanese‑Qwen2.5‑32B‑V1.0 」（平均スコア：0.7417）
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ベースモデルである Qwen2.5‑32B‑Instruct と比較すると、本モデルはほとんどのタスクで性能が大幅に向上し、特に FA（Fundamental Analysis／基礎分析）、SUM（Summarization／要約）、CG（Code Generation／コード生成） において顕著な改善が見られます。
|
||||
|
||||
| Tasks | Qwen2.5-32B-Instruct | D2IL-Japanese-Qwen2.5-32B-Instruct-v0.1 | Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 |
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||||
|------------------------------------|----------------------|-----------------------------------------|------------------------------------------|
|
||||
| NLI - 自然言語推論 | 0.8106 | 0.8793 | 0.8846 (+0.0740) |
|
||||
| QA - 質問応答 | 0.541 | 0.5897 | 0.5965 (+0.0555) |
|
||||
| RC - 読解力 | 0.9047 | 0.9005 | 0.9261 (+0.0214) |
|
||||
| MC - 多肢選択式質問応答 | 0.8966 | 0.9139 | 0.9128 (+0.0162) |
|
||||
| EL - エンティティリンキン | 0.5894 | 0.6782 | 0.6975 (+0.1081) |
|
||||
| FA - 基礎分析 | 0.2737 | 0.4321 | 0.5185 (+0.2448) |
|
||||
| MR - 数学的推論 | 0.944 | 0.938 | 0.9420 (-0.0020) |
|
||||
| MT - 機械翻訳 | 0.8479 | 0.7954 | 0.8389 (-0.0090) |
|
||||
| HE - 試験問題 | 0.7757 | 0.7902 | 0.7987 (+0.0230) |
|
||||
| CG - コード生成 | 0.5281 | 0.6084 | 0.7610 (+0.2329) |
|
||||
| SUM - 要約 | 0.097 | 0.2843 | 0.2827 (+0.1857) |
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||||
| **Average** | **0.6553** | **0.71** | **0.7417 (+0.0864)** |
|
||||
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# 🚀 一貫した汎用性能
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Flux‑Japanese‑Qwen2.5‑32B‑Instruct‑V1.0は学習により日本語能力が大幅に改善した一方で、汎用的なタスクや英語でのタスク遂行能力においても性能を維持しており、ベースモデルである「Qwen2.5-32B-Instruct」と比較して、1%以下というごく僅かな差にとどまっています。評価は [simple-evals](https://github.com/deep-analysis-research/simple-evals) に基づいて行われています。
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| Tasks | Dataset | Qwen2.5-32B-Instruct | Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 |
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||||
|---------------------|----------------|----------------------|------------------------------------------|
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| General Tasks | MMLU-redux | 80.37 | 80.03 (-0.34) |
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| | GPQGA-Diamond | 46.11 | 47.32 (+1.21) |
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| | MMLU | 82.84 | 83.39 (+0.55) |
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| Math Tasks | MATH-500 | 78.14 | 78.50 (+0.36) |
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| | AIME24 | 17.06 | 17.92 (+0.86) |
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| | AIME25 | 16.25 | 14.58 (-1.67) |
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| | MT-AIME24 | 12.73 | 12.97 (+0.24) |
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| Multilingual Tasks | Multi-IF | 71.85 | 63.45 (-8.40) |
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| | INCLUDE | 65.16 | 64.64 (-0.52) |
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| | MMMLU | 73.43 | 74.08 (+0.65) |
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| Coding Tasks | HumanEval | 87.93 | 86.51 (-1.42) |
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| Alignment Tasks | IFEval | 78.37 | 77.46 (-0.91) |
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| **Average** | | **59.17** | **58.40 (-0.77)** |
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# ⚙️ 技術開発
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<center><img src="tech-dev.png" alt="technical-development"/></center>
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- **Phase 1: Interpretability Analysis & Pinpoint Tuning** — 日本語能力（知識・推論・言語）向上のために、Mechanistic Interpretability (MI)技術を活用することで、特定のパスと回路を識別し、僅か5%のパラメータに対してPinpoint Tuningを実施する方法を採用しました。これにより日本語の知識・推論・言語のそれぞれの能力に特化した3つのエキスパートモデルを作成しました。
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- **Phase 2: Pinpoint Merging** — 3つのエキスパートモデルに対し、Pinpoint Merging を実施し、日本語の知識・推論・言語の各分野でエキスパートレベルの性能を備えた”日本語特化”統合モデルを構築します。 [[Pinpoint Merging のコード](https://github.com/deep-analysis-research/SLTA)].
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# 🚩 クイックスタート
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
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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("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
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prompt = "大規模言語モデルについて簡単に紹介してください。"
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messages = [
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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(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=512
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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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```
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# 💡 利用規約
|
||||
本モデルは、バイアスや有害な応答をはじめとする各種リスクを低減するため、様々な技術的アプローチを用いて、その安全性と信頼性の向上に努めております。
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||||
しかし、本モデルを含む全ての大規模言語モデル（LLM）には、不正確な情報、誤解を招く内容、あるいは偏見を反映した、意図しない応答を生成する可能性が依然として存在します。本モデルをダウンロード、利用、または対話形式で使用することにより、利用者は以下の事項を理解し、これに同意したものとみなされます。
|
||||
1. 禁止事項
|
||||
- 利用者は、本モデルを、詐欺、濫用、嫌がらせ、プライバシー侵害、悪意のあるコンテンツの作成／拡散などを含む、違法、または法令に違反する悪質な活動に利用することは禁止します。
|
||||
2. 利用者の責任
|
||||
- 本モデルの使用方法及びその利用に起因して生じるすべての事象については、利用者自身が単独で責任を負うこととします。
|
||||
- 本モデルの公開に関与した作成者・公開者及び機関は、その使用に起因して生じるいかなる結果についても、一切の責任を負いません。
|
||||
3. 保証
|
||||
- 本モデルは現状の状態で提供され、いかなる保証も伴いません。
|
||||
104
README.md
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README.md
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---
|
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license: apache-2.0
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||||
license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE
|
||||
language:
|
||||
- en
|
||||
pipeline_tag: text-generation
|
||||
base_model: Qwen/Qwen2.5-32B
|
||||
tags:
|
||||
- chat
|
||||
library_name: transformers
|
||||
---
|
||||
|
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# Flux-Japanese-Qwen2.5-32B-Instruct-V1.0
|
||||
[**English**] [[Japanese](./README-ja.md)]
|
||||
|
||||
Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 is a 32 billion parameter open-weights models with strong performance in Japanese knowledge, reasoning and language. It is trained based on the Qwen2.5-32B-Instruct and under the Apache 2.0 opensource licence
|
||||
|
||||
# 🏆 Open-Japanese-LLM-Leaderboard Rank-1
|
||||
On the [Open LLM Japanese LLM Leaderboard](https://huggingface.co/spaces/deep-analysis-research/open-japanese-llm-leaderboard), Qwen2.5-32B-Instruct scores 0.6553, compared to the former top-ranked D2IL-Japanese-Qwen2.5-32B-Instruct-v0.1 at 0.7100, and Flux-Japanese-Qwen2.5-32B-V1.0 at 0.7417. Compared with the original Qwen2.5-32B-Instruct, Flux-Japanese-Qwen2.5-32B-v1.0 demonstrates significant gains across most tasks, with especially strong improvements in FA (Fundamental Analysis, 基礎分析), SUM (Summarization, 要約), and CG (Code Generation, コード生成).
|
||||
| Tasks | Qwen2.5-32B-Instruct | D2IL-Japanese-Qwen2.5-32B-Instruct-v0.1 | Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 |
|
||||
|------------------------------------|----------------------|-----------------------------------------|------------------------------------------|
|
||||
| NLI - 自然言語推論 | 0.8106 | 0.8793 | 0.8846 (+0.0740) |
|
||||
| QA - 質問応答 | 0.541 | 0.5897 | 0.5965 (+0.0555) |
|
||||
| RC - 読解力 | 0.9047 | 0.9005 | 0.9261 (+0.0214) |
|
||||
| MC - 多肢選択式質問応答 | 0.8966 | 0.9139 | 0.9128 (+0.0162) |
|
||||
| EL - エンティティリンキン | 0.5894 | 0.6782 | 0.6975 (+0.1081) |
|
||||
| FA - 基礎分析 | 0.2737 | 0.4321 | 0.5185 (+0.2448) |
|
||||
| MR - 数学的推論 | 0.944 | 0.938 | 0.9420 (-0.0020) |
|
||||
| MT - 機械翻訳 | 0.8479 | 0.7954 | 0.8389 (-0.0090) |
|
||||
| HE - 試験問題 | 0.7757 | 0.7902 | 0.7987 (+0.0230) |
|
||||
| CG - コード生成 | 0.5281 | 0.6084 | 0.7610 (+0.2329) |
|
||||
| SUM - 要約 | 0.097 | 0.2843 | 0.2827 (+0.1857) |
|
||||
| **Average** | **0.6553** | **0.71** | **0.7417 (+0.0864)** |
|
||||
|
||||
|
||||
# 🚀 Consistent General Performance
|
||||
While Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 has been specifically tuned for Japanese, its performance on general capabilities and English tasks remains within 1% of Qwen2.5-32B-Instruct, indicating negligible impact. The evaluation is based on [simple-evals](https://github.com/deep-analysis-research/simple-evals).
|
||||
|
||||
| Tasks | Dataset | Qwen2.5-32B-Instruct | Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 |
|
||||
|---------------------|----------------|----------------------|------------------------------------------|
|
||||
| General Tasks | MMLU-redux | 80.37 | 80.03 (-0.34) |
|
||||
| | GPQGA-Diamond | 46.11 | 47.32 (+1.21) |
|
||||
| | MMLU | 82.84 | 83.39 (+0.55) |
|
||||
| Math Tasks | MATH-500 | 78.14 | 78.50 (+0.36) |
|
||||
| | AIME24 | 17.06 | 17.92 (+0.86) |
|
||||
| | AIME25 | 16.25 | 14.58 (-1.67) |
|
||||
| | MT-AIME24 | 12.73 | 12.97 (+0.24) |
|
||||
| Multilingual Tasks | Multi-IF | 71.85 | 63.45 (-8.40) |
|
||||
| | INCLUDE | 65.16 | 64.64 (-0.52) |
|
||||
| | MMMLU | 73.43 | 74.08 (+0.65) |
|
||||
| Coding Tasks | HumanEval | 87.93 | 86.51 (-1.42) |
|
||||
| Alignment Tasks | IFEval | 78.37 | 77.46 (-0.91) |
|
||||
| **Average** | | **59.17** | **58.40 (-0.77)** |
|
||||
|
||||
|
||||
# ⚙️ Technical Development
|
||||
|
||||
<center><img src="tech-dev.png" alt="technical-development"/></center>
|
||||
|
||||
- **Phase 1: Interpretability Analysis & Pinpoint Tuning** — For Japanese Knowledge, Reasoning, and Language, leverage mechanistic interpretability techniques to identify independent pathways/circuits, and apply targeted pinpoint tuning to only 5% of the parameters. This produces three expert models specialized respectively in Japanese knowledge, reasoning, and language.
|
||||
- **Phase 2: Pinpoint Merging** — Perform pinpoint parameter merging on the three expert models to obtain a unified model that reaches expert-level performance across Japanese knowledge, reasoning, and language [[Code of Pinpoint Merging](https://github.com/deep-analysis-research/SLTA)].
|
||||
|
||||
# 🚩 Quickstart
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
device = "cuda" # the device to load the model onto
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
|
||||
torch_dtype="auto",
|
||||
device_map="auto"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
|
||||
|
||||
prompt = "大規模言語モデルについて簡単に紹介してください。"
|
||||
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(device)
|
||||
|
||||
generated_ids = model.generate(
|
||||
model_inputs.input_ids,
|
||||
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]
|
||||
```
|
||||
# 💡 Terms of Use
|
||||
We have employed various techniques to reduce bias, harmful outputs, and other risks in the model. While these efforts help improve safety and reliability, the model, like all Large Language Models, may still generate inaccurate, misleading, biased, or otherwise undesirable content. By downloading, using, or interacting with this model, you acknowledge these limitations and agree to the following:
|
||||
1. Prohibited Uses
|
||||
- You may NOT use this model for any illegal, unlawful, or harmful activities, including but not limited to fraud, abuse, harassment, privacy violations, or the creation/dissemination of malicious content.
|
||||
2. User Responsibility
|
||||
- You are solely responsible for how you use the model and for any outcomes that result from its use.
|
||||
- The authors and institutions involved in releasing this model do NOT accept liability for any consequences arising from its use.
|
||||
3. No Warranty
|
||||
- The model is provided “as is” without any warranties or guarantees.
|
||||
24
added_tokens.json
Normal file
24
added_tokens.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"</tool_call>": 151658,
|
||||
"<tool_call>": 151657,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. 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 Qwen, created by Alibaba Cloud. You 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 %}
|
||||
28
config.json
Normal file
28
config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 5120,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 27648,
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 64,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 131072,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.4",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.52.4"
|
||||
}
|
||||
151387
merges.txt
Normal file
151387
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00014.safetensors
Normal file
3
model-00001-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
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||||
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|
||||
size 4891730992
|
||||
3
model-00002-of-00014.safetensors
Normal file
3
model-00002-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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|
||||
3
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Normal file
3
model-00003-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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||||
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||||
size 4876059384
|
||||
3
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Normal file
3
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Normal file
@@ -0,0 +1,3 @@
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||||
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size 4876059416
|
||||
3
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Normal file
3
model-00005-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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|
||||
size 4876059416
|
||||
3
model-00006-of-00014.safetensors
Normal file
3
model-00006-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:961b14b120ab824921cf61a1705f454e6a2d6712115052c3625a7d20d44732cd
|
||||
size 4876059416
|
||||
3
model-00007-of-00014.safetensors
Normal file
3
model-00007-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4876059416
|
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3
model-00008-of-00014.safetensors
Normal file
3
model-00008-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4876059416
|
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3
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Normal file
3
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Normal file
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version https://git-lfs.github.com/spec/v1
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|
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3
model-00010-of-00014.safetensors
Normal file
3
model-00010-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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3
model-00011-of-00014.safetensors
Normal file
3
model-00011-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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3
model-00012-of-00014.safetensors
Normal file
3
model-00012-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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3
model-00013-of-00014.safetensors
Normal file
3
model-00013-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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model-00014-of-00014.safetensors
Normal file
3
model-00014-of-00014.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 2123397800
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778
model.safetensors.index.json
Normal file
778
model.safetensors.index.json
Normal file
@@ -0,0 +1,778 @@
|
||||
{
|
||||
"metadata": {
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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>\" }}\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 {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) 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{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user