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Model: demimomi/dpo-qwen-cot-merged Source: Original Platform
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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99
README.md
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README.md
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---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- dpo_train_brushed_v4_balanced.json
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- dpo
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- unsloth
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- qwen
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- alignment
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---
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# 東京大学 松尾・岩澤研究室 大規模言語モデル 応用講座2025-2026
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## Author and Acknowledgments
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- **Author:** Toshiki Demizu (出水 利樹) — GitHub/Hugging Face ID: [@demimomi](https://huggingface.co/demimomi)
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- **Affiliation:** ソフトバンク株式会社、MONET Technologies株式会社
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- **Course:** Large Language Model Development Lecture Advanced (Winter 2025－2026)
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- **Participants:** 3800名参加
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## メインコンペ(2026年2月2日～3月2日)
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- **状況:**
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2026年2月8日現在 293位（現時点で497人が提出） 0.70044点
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|
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2026年2月11日現在 261位（現時点で646人が提出）0.73407点
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https://huggingface.co/demimomi/demimomi44taomax-qwen3-4b-structured-output-lora
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T4（TPU）だと日次Limitにすぐ達するため、A100（GPU）にて学習/推論コードを実施。
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|
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- **ルール:**
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基準点：0.7 ※コード脳死で回すだけでは超えられない
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||||
１Google Colabで実行可能なモデル・実装であること
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２評価は StructEval（Text）のみを使用
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３提出物は推論結果JSONとHugging Face上のモデルURL
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４運営指定モデル・データのみ使用可
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５Omnicampusに提出すると自動採点・順位付け
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# ＜demimomi-max44-qwen3-4b-dpo-qwen-cot-merged（ 0.70044点版）＞
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## model
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This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
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This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
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## Training Objective
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This model has been optimized using DPO to align its responses with preferred outputs, focusing on improving reasoning (Chain-of-Thought) and structured response quality based on the provided preference dataset.
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## Training Configuration
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- **Base model**: Qwen/Qwen3-4B-Instruct-2507
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- **Method**: DPO (Direct Preference Optimization)
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- **Epochs**: 2
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- **Learning rate**: 1e-06
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- **Beta**: 0.05
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- **Max sequence length**: 1536
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- **LoRA Config**: r=8, alpha=16 (merged into base)
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## Usage
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Since this is a merged model, you can use it directly with `transformers`.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "demimomi/dpo-qwen-cot-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Test inference
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prompt = "Your question here"
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inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0]))
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|
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```
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|
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## Sources & License (IMPORTANT)
|
||||
|
||||
* **Training Data**: [dpo_train_brushed_v4_balanced.json]
|
||||
* **License**: MIT License. (As per dataset terms).
|
||||
* **Compliance**: Users must follow the original base model's license terms.
|
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28
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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||||
"<tool_response>": 151665,
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"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
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||||
"<|file_sep|>": 151664,
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||||
"<|fim_middle|>": 151660,
|
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"<|fim_pad|>": 151662,
|
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"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
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||||
"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
|
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53
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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||||
{{- '<|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.' }}
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||||
{%- 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 %}
|
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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 %}
|
||||
{%- 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' }}
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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 %}
|
||||
{%- 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 %}
|
||||
70
config.json
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config.json
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||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 9728,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 262144,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151654,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 5000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "4.56.2",
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2025.12.7",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
132
dpo_train_brushed_v4_balanced.json
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dpo_train_brushed_v4_balanced.json
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|
||||
[
|
||||
{
|
||||
"prompt": "以下のCSVデータをJSON形式に変換してください。出力は**JSONのみ**とし、説明文やMarkdownは含めないでください。\n\nCSV:\nname,model,length_m\nStar Voyager,XJ-9,120",
|
||||
"chosen": "[{\"name\":\"Star Voyager\",\"model\":\"XJ-9\",\"length_m\":120}]",
|
||||
"rejected": "```json\n[{\"name\":\"Star Voyager\",\"model\":\"XJ-9\",\"length_m\":120}]\n```"
|
||||
},
|
||||
{
|
||||
"prompt": "次の情報を指定されたスキーマに従ってJSON形式で出力してください。出力は**JSONのみ**で、前置き・説明・コードブロックは禁止です。\n\nスキーマ:\n{ \"artifact\": { \"name\": string } }\n\n入力:\nartifact name = Rosetta Stone",
|
||||
"chosen": "{\"artifact\":{\"name\":\"Rosetta Stone\"}}",
|
||||
"rejected": "以下がJSON形式の出力です。\n```json\n{\"artifact\":{\"name\":\"Rosetta Stone\"}}\n```"
|
||||
},
|
||||
{
|
||||
"prompt": "与えられたデータをJSON形式で整理して出力してください。JSON以外のテキストは含めないでください。\n\nデータ:\n- ecosystem: Luminara Rainforest\n- climate.type: tropical",
|
||||
"chosen": "{\"ecosystem\":{\"name\":\"Luminara Rainforest\",\"climate\":{\"type\":\"tropical\"}}}",
|
||||
"rejected": "Here is the JSON representation of the data:\n{\"ecosystem\":{\"name\":\"Luminara Rainforest\",\"climate\":{\"type\":\"tropical\"}}}"
|
||||
},
|
||||
{
|
||||
"prompt": "以下の条件に従ってJSON形式で回答してください。余分な説明は不要です。\n\n条件:\n- id=1\n- status=active\n- 配列で返すこと",
|
||||
"chosen": "[{\"id\":1,\"status\":\"active\"}]",
|
||||
"rejected": "もちろんです。ご要望のJSONはこちらです。\n[{\"id\":1,\"status\":\"active\"}]"
|
||||
},
|
||||
{
|
||||
"prompt": "次のリストをJSON配列にしてください。出力はJSONのみ。\n\nItems:\n1) apple\n2) banana\n3) cherry",
|
||||
"chosen": "[\"apple\",\"banana\",\"cherry\"]",
|
||||
"rejected": "```json\n[\"apple\",\"banana\",\"cherry\"]\n```"
|
||||
},
|
||||
{
|
||||
"prompt": "次の人物データをJSONで出力してください。出力はJSONのみ。\n\nname: 山田太郎\nage: 29\nis_student: false",
|
||||
"chosen": "{\"name\":\"山田太郎\",\"age\":29,\"is_student\":false}",
|
||||
"rejected": "```json\n{\"name\":\"山田太郎\",\"age\":29,\"is_student\":false}\n```"
|
||||
},
|
||||
{
|
||||
"prompt": "次のキーと値をJSONオブジェクトにしてください（値は数値）。出力はJSONのみ。\n\nheight_cm=175\nweight_kg=68",
|
||||
"chosen": "{\"height_cm\":175,\"weight_kg\":68}",
|
||||
"rejected": "[{\"height_cm\":175,\"weight_kg\":68}]"
|
||||
},
|
||||
{
|
||||
"prompt": "以下のYAMLを同じ内容のJSONに変換してください。出力はJSONのみ。\n\nyaml:\nserver:\n host: \"example.com\"\n port: 443\n tls: true",
|
||||
"chosen": "{\"server\":{\"host\":\"example.com\",\"port\":443,\"tls\":true}}",
|
||||
"rejected": "{\"server\":{\"host\":\"example.com\",\"port\":443,\"tls\":true,}}"
|
||||
},
|
||||
{
|
||||
"prompt": "次の注文情報を指定スキーマでJSON出力してください。JSON以外は禁止。\n\nスキーマ:\n{order:{id:int,items:[{sku:string,qty:int}]}}\n\n入力:\norder id: 1001\nitems:\n- sku: A-12, qty: 2\n- sku: B-07, qty: 1",
|
||||
"chosen": "{\"order\":{\"id\":1001,\"items\":[{\"sku\":\"A-12\",\"qty\":2},{\"sku\":\"B-07\",\"qty\":1}]}}",
|
||||
"rejected": "```json\n{\"order\":{\"id\":1001,\"items\":[{\"sku\":\"A-12\",\"qty\":2},{\"sku\":\"B-07\",\"qty\":1}]}}\n```\n(以上)"
|
||||
},
|
||||
{
|
||||
"prompt": "次の文章から、抽出した情報をJSONで返してください。出力はJSONのみ。\n\n文章:\n\"会議は2026-02-10 15:30に東京で実施。参加者はAさんとBさん。\"\n\n出力スキーマ:\n{date:string,time:string,location:string,attendees:[string]}",
|
||||
"chosen": "{\"date\":\"2026-02-10\",\"time\":\"15:30\",\"location\":\"東京\",\"attendees\":[\"Aさん\",\"Bさん\"]}",
|
||||
"rejected": "{\"date\":\"2026-02-10\",\"time\":\"15:30\",\"location\":\"東京\",\"attendees\":[\"Aさん\",\"Bさん\"],\"_note\":\"extra\"}"
|
||||
},
|
||||
{
|
||||
"prompt": "次のSQL結果（ヘッダー付き）をJSON配列に変換してください。JSONのみ。\n\nid,name,score\n1,Alice,88.5\n2,Bob,92.0",
|
||||
"chosen": "[{\"id\":1,\"name\":\"Alice\",\"score\":88.5},{\"id\":2,\"name\":\"Bob\",\"score\":92.0}]",
|
||||
"rejected": "[{\"id\":1,\"name\":\"Alice\",\"score\":88.5}, {\"id\":2,\"name\":\"Bob\",\"score\":92.0}] // converted"
|
||||
},
|
||||
{
|
||||
"prompt": "次の設定をJSONで出力してください。出力はJSONのみ。\n\nmode= \"auto\"\nretry= 3\ntimeout_sec= 0.5\nnotes= null",
|
||||
"chosen": "{\"mode\":\"auto\",\"retry\":3,\"timeout_sec\":0.5,\"notes\":null}",
|
||||
"rejected": "{\"mode\":\"auto\",\"retry\":3,\"timeout_sec\":.5,\"notes\":null}"
|
||||
},
|
||||
{
|
||||
"prompt": "以下の入力から、指定スキーマのJSONだけを出力してください。\n\n入力:\nproduct: \"USB-C Cable\"\nprice: 1299\ncurrency: \"JPY\"\n\nスキーマ:\n{product:{name:string,price:{amount:int,currency:string}}}",
|
||||
"chosen": "{\"product\":{\"name\":\"USB-C Cable\",\"price\":{\"amount\":1299,\"currency\":\"JPY\"}}}",
|
||||
"rejected": "{\"product\":{\"name\":\"USB-C Cable\",\"price\":{\"amount\":1299,\"currency\":\"JPY\"}},\"name\":\"USB-C Cable\"}"
|
||||
},
|
||||
{
|
||||
"prompt": "次の2つの座標をJSONで返してください（配列）。JSONのみ。\n\n(35.6895,139.6917)\n(34.6937,135.5023)",
|
||||
"chosen": "[[35.6895,139.6917],[34.6937,135.5023]]",
|
||||
"rejected": "JSON:\n[[35.6895,139.6917],[34.6937,135.5023]]"
|
||||
},
|
||||
{
|
||||
"prompt": "次の入力をJSONにしてください。出力はJSONのみ。\n\n入力:\nkey=enabled\nvalue=true\n\n出力スキーマ:\n{enabled:boolean}",
|
||||
"chosen": "{\"enabled\":true}",
|
||||
"rejected": "{'enabled': true}"
|
||||
},
|
||||
{
|
||||
"prompt": "次のログからエラー情報だけをJSONで抽出してください。JSONのみ。\n\nLOG:\n[INFO] start\n[ERROR] code=E42 msg=\"timeout\" retry=1\n[INFO] end\n\nスキーマ:\n{code:string,message:string,retry:int}",
|
||||
"chosen": "{\"code\":\"E42\",\"message\":\"timeout\",\"retry\":1}",
|
||||
"rejected": "```json\n{\"code\":\"E42\",\"message\":\"timeout\",\"retry\":1}\n```"
|
||||
},
|
||||
{
|
||||
"prompt": "以下の表をJSON配列にしてください。出力はJSONのみ。\n\n| id | active |\n| 7 | yes |\n| 8 | no |\n\nルール:\n- yes→true, no→false",
|
||||
"chosen": "[{\"id\":7,\"active\":true},{\"id\":8,\"active\":false}]",
|
||||
"rejected": "{\"id\":7,\"active\":true}"
|
||||
},
|
||||
{
|
||||
"prompt": "次の入力から、JSONのみを返してください。\n\n入力:\ncolors = red, green, blue\n\n出力スキーマ:\n{colors:[string]}",
|
||||
"chosen": "{\"colors\":[\"red\",\"green\",\"blue\"]}",
|
||||
"rejected": "{\"colors\":[\"red\",\"green\",\"blue\"],}"
|
||||
},
|
||||
{
|
||||
"prompt": "次の住所をJSONで構造化してください。JSONのみ。\n\n住所: 〒100-0001 東京都千代田区千代田1-1\n\nスキーマ:\n{postal_code:string,prefecture:string,city:string,detail:string}",
|
||||
"chosen": "{\"postal_code\":\"100-0001\",\"prefecture\":\"東京都\",\"city\":\"千代田区\",\"detail\":\"千代田1-1\"}",
|
||||
"rejected": "出力:\n{\"postal_code\":\"100-0001\",\"prefecture\":\"東京都\",\"city\":\"千代田区\",\"detail\":\"千代田1-1\"}"
|
||||
},
|
||||
{
|
||||
"prompt": "次のデータをJSONで返してください。出力はJSONのみ。数値は数値型にしてください。\n\nitem=book\nqty=2\nunit_price=450\n\nスキーマ:\n{item:string,qty:int,total:int}",
|
||||
"chosen": "{\"item\":\"book\",\"qty\":2,\"total\":900}",
|
||||
"rejected": "{\"item\":\"book\",\"qty\":2,\"total\":900,\"_note\":\"extra\"}"
|
||||
},
|
||||
{
|
||||
"prompt": "次の入力から、指定スキーマのJSONを作ってください。JSONのみ。\n\n入力:\nstart=2026-03-01\nend=2026-03-05\n\nスキーマ:\n{period:{start:string,end:string,days:int}}\n\n補足:\n- days は end-start の日数（両端含めない）",
|
||||
"chosen": "{\"period\":{\"start\":\"2026-03-01\",\"end\":\"2026-03-05\",\"days\":4}}",
|
||||
"rejected": "```json\n{\"period\":{\"start\":\"2026-03-01\",\"end\":\"2026-03-05\",\"days\":4}}\n```"
|
||||
},
|
||||
{
|
||||
"prompt": "次のテキストをJSON文字列としてエスケープして出力してください。出力はJSONのみ。\n\ntext: He said \"Hello\".\n\nスキーマ:\n{text:string}",
|
||||
"chosen": "{\"text\":\"He said \\\"Hello\\\".\"}",
|
||||
"rejected": "{\"text\":\"He said \"Hello\".\"}"
|
||||
},
|
||||
{
|
||||
"prompt": "次の入力をJSONにしてください（null の扱いに注意）。出力はJSONのみ。\n\nname=Akira\nmiddle_name=(none)\n\nスキーマ:\n{name:string,middle_name:null}",
|
||||
"chosen": "{\"name\":\"Akira\",\"middle_name\":null}",
|
||||
"rejected": "Sure! Here is the JSON:\n{\"name\":\"Akira\",\"middle_name\":null}"
|
||||
},
|
||||
{
|
||||
"prompt": "以下の条件でJSONを出力してください。出力はJSONのみ。\n\n条件:\n- tags は重複を除いてアルファベット順\n入力 tags: beta, alpha, beta",
|
||||
"chosen": "{\"tags\":[\"alpha\",\"beta\"]}",
|
||||
"rejected": "JSON:\n{\"tags\":[\"alpha\",\"beta\"]}"
|
||||
},
|
||||
{
|
||||
"prompt": "次のデータをJSONで出力してください。出力はJSONのみ。末尾に句点や改行以外の文字を付けないでください。\n\nk=v",
|
||||
"chosen": "{\"k\":\"v\"}",
|
||||
"rejected": "{\"k\":\"v\"}。"
|
||||
},
|
||||
{
|
||||
"prompt": "次の入力を指定スキーマでJSON出力してください。JSONのみ。\n\n入力:\nitems:\n- id: 1\n ok: true\n- id: 2\n ok: false\n\nスキーマ:\n{items:[{id:int,ok:boolean}]}",
|
||||
"chosen": "{\"items\":[{\"id\":1,\"ok\":true},{\"id\":2,\"ok\":false}]}",
|
||||
"rejected": "{}"
|
||||
}
|
||||
]
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:a020e79d4f1ad19a260cb9d1f8a38ea0b5c080ebb36d782e1834423d27bcd304
|
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size 4967215360
|
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3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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oid sha256:66ad94889435e04799a1546675256ad9e6b521d632c0f12bb098f4840ff409d2
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||||
size 3077766632
|
||||
406
model.safetensors.index.json
Normal file
406
model.safetensors.index.json
Normal file
@@ -0,0 +1,406 @@
|
||||
{
|
||||
"metadata": {
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||||
"total_parameters": 4022468096,
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"total_size": 8044936192
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31
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Normal file
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3
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241
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241
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Normal file
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|
||||
"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\") %} {{- '<|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"
|
||||
}
|
||||
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