LlaMA3-SFT
LlaMA3-SFT, Meta-Llama-3-8B/Meta-Llama-3-8B-Instruct微调(transformers)/LORA(peft)/推理
项目地址
https://github.com/yongzhuo/LLaMA3-SFT 默认数据类型为bfloat6备注
1. 非常重要: weights要用bfloat16/fp32/tf32(第二版大模型基本共识), 不要用fp16, fp16会特别容易loss=NAN;
2. SFT最好还是像预训练那样, input/output都计算loss;
2. transformers需要4.40.0及以上;
3. llama3, 模型的词典大小为128256, 使用tiktoken;
4. llama3网络架构同Llama2, 使用GQA/MQA等加速;
5. prompt:
5.1 标准格式为:
text_input + text_output
5.2 prompt格式为:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{text_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{text_output}<|eot_id|>
6 微调输入输出:
输入："<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{text_input}<|eot_id|>"
输出："<|start_header_id|>assistant<|end_header_id|>\n\n{text_output}<|eot_id|>"
7 推理输入输出(assistant\n放置位置不同):
输入："<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{text_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
输出："{text_output}<|eot_id|>"
8. 网络各层名称
('base_model.model.model.embed_tokens.weight', torch.bfloat16, False)
......
('base_model.model.model.layers.31.self_attn.q_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.self_attn.k_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.self_attn.v_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.self_attn.o_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.mlp.gate_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.mlp.up_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.mlp.down_proj.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.input_layernorm.weight', torch.bfloat16, False)
('base_model.model.model.layers.31.post_attention_layernorm.weight', torch.bfloat16, False)
('base_model.model.model.norm.weight', torch.bfloat16, False)
('base_model.model.lm_head.weight', torch.bfloat16, False)
9. RuntimeError: unscale_() has already been called on this optimizer since the last update().
微调语料太少导致的
环境配置
transformers>=4.44.0
torch>=1.13.0
safetensors>=0.4.1
accelerate==0.27.1
fsspec==2023.9.2
rouge==1.0.1
nltk==3.6.6
peft>=0.2.0
tiktoken
numpy
tqdm
微调
地址: llama3_sft/ft_llama3
配置: llama3_sft/ft_llama3/config.py
训练: python train.py
推理: python predict.py
验证: python evaluation.py
接口: python post_api.py
数据集-中文
https://huggingface.co/datasets/JosephusCheung/GuanacoDataset https://huggingface.co/datasets/shareAI/shareGPT_cn https://huggingface.co/datasets/Mutonix/RefGPT-Fact https://huggingface.co/datasets/BAAI/COIG https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM https://github.com/carbonz0/alpaca-chinese-dataset https://github.com/LianjiaTech/BELLE https://github.com/PhoebusSi/Alpaca-CoT https://github.com/Hello-SimpleAI/chatgpt-comparison-detection https://github.com/yangjianxin1/Firefly https://github.com/XueFuzhao/InstructionWild https://github.com/OpenLMLab/MOSS https://github.com/thu-coai/Safety-Prompts https://github.com/LAION-AI/Open-Assistant https://github.com/TigerResearch/TigerBot参考/感谢
https://github.com/meta-llama/llama3 https://github.com/QwenLM/Qwen1.5 https://github.com/google/gemma_pytorch https://huggingface.co/google/gemma-2b-it https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard https://github.com/THUDM/ChatGLM-6B https://github.com/THUDM/GLM https://github.com/tatsu-lab/stanford_alpaca https://github.com/LianjiaTech/BELLE https://github.com/huggingface/peft https://github.com/mymusise/ChatGLM-Tuning https://github.com/bojone/bert4keras trl math23k微调日志-advgen
默认的bf16微调, 计算全部loss(同pt, input/output), loss太低
推理日志-advgen(SFT), 感觉学到了一些东西, 又没有学会
('base_model.model.base_model.model.model.layers.31.self_attn.q_proj.weight', torch.bfloat16, False, tensor([[ 0.0078, -0.0035, 0.0075, ..., -0.0007, -0.0016, -0.0020],
[ 0.0036, -0.0019, 0.0140, ..., -0.0161, -0.0037, 0.0012],
[ 0.0080, 0.0125, -0.0103, ..., -0.0005, -0.0267, 0.0027],
...,
[-0.0061, 0.0166, 0.0148, ..., -0.0105, -0.0165, 0.0242],
[-0.0181, -0.0244, 0.0100, ..., -0.0334, 0.0020, -0.0227],
[-0.0042, -0.0181, 0.0114, ..., 0.0027, -0.0004, -0.0002]],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.self_attn.q_proj.lora_A.default.weight', torch.float32, False, tensor([[-0.0188, 0.0052, -0.0048, ..., -0.0073, 0.0091, 0.0070],
[-0.0117, -0.0048, -0.0020, ..., 0.0009, 0.0169, 0.0139],
[ 0.0011, -0.0023, -0.0166, ..., -0.0071, -0.0082, 0.0082],
...,
[ 0.0127, -0.0045, -0.0137, ..., -0.0146, 0.0114, -0.0145],
[ 0.0038, -0.0148, 0.0001, ..., -0.0172, -0.0139, 0.0062],
[-0.0056, 0.0132, 0.0115, ..., 0.0100, 0.0127, 0.0004]]))
('base_model.model.base_model.model.model.layers.31.self_attn.q_proj.lora_B.default.weight', torch.float32, False, tensor([[ 6.0165e-04, -1.8864e-04, 5.4384e-04, ..., 3.9676e-04,
-1.9826e-04, -7.4172e-05],
[ 2.4563e-03, -2.3599e-03, 2.0984e-03, ..., 1.9662e-03,
1.5514e-03, -1.8005e-03],
[-4.9125e-03, 4.7831e-03, -4.6263e-03, ..., -4.3837e-03,
-3.4190e-03, 4.2660e-03],
...,
[ 1.2281e-05, 5.1583e-04, -1.4735e-04, ..., 1.3617e-04,
-9.5673e-04, 8.3033e-04],
[-1.5139e-03, 3.0022e-03, -2.4098e-03, ..., -1.9860e-03,
-2.3010e-03, 2.5711e-03],
[ 1.6433e-03, -2.0675e-03, 2.2618e-03, ..., 1.4840e-03,
1.1958e-03, -1.3300e-03]]))
('base_model.model.base_model.model.model.layers.31.self_attn.k_proj.weight', torch.bfloat16, False, tensor([[ 0.0126, -0.0269, 0.0297, ..., 0.0481, -0.0057, 0.0061],
[-0.0262, -0.0154, -0.0427, ..., -0.0217, -0.0513, 0.0092],
[ 0.0098, 0.0058, 0.0157, ..., -0.0054, -0.0025, -0.0024],
...,
[-0.0198, 0.0408, -0.0136, ..., -0.0173, -0.0432, 0.0082],
[ 0.0312, 0.0239, 0.0247, ..., -0.0171, -0.0284, -0.0432],
[-0.0096, 0.0043, 0.0142, ..., -0.0164, -0.0386, 0.0206]],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.self_attn.k_proj.lora_A.default.weight', torch.float32, False, tensor([[ 0.0137, -0.0115, 0.0137, ..., -0.0069, -0.0056, -0.0032],
[-0.0117, -0.0096, -0.0165, ..., -0.0014, 0.0045, -0.0028],
[ 0.0072, -0.0115, 0.0109, ..., -0.0169, -0.0165, 0.0081],
...,
[-0.0194, 0.0073, -0.0093, ..., 0.0050, -0.0120, 0.0028],
[-0.0064, -0.0142, -0.0112, ..., -0.0126, 0.0087, 0.0097],
[-0.0108, 0.0042, -0.0046, ..., 0.0105, -0.0111, -0.0107]]))
('base_model.model.base_model.model.model.layers.31.self_attn.k_proj.lora_B.default.weight', torch.float32, False, tensor([[ 0.0024, -0.0033, -0.0026, ..., -0.0024, -0.0035, -0.0017],
[ 0.0016, -0.0022, -0.0032, ..., -0.0017, -0.0023, 0.0015],
[-0.0028, 0.0032, 0.0029, ..., 0.0027, 0.0034, 0.0017],
...,
[-0.0015, -0.0001, 0.0005, ..., 0.0001, 0.0006, -0.0047],
[ 0.0022, 0.0023, -0.0005, ..., -0.0007, -0.0013, 0.0028],
[-0.0003, -0.0020, -0.0002, ..., -0.0003, -0.0003, -0.0021]]))
('base_model.model.base_model.model.model.layers.31.self_attn.v_proj.weight', torch.bfloat16, False, tensor([[ 0.0172, 0.0181, -0.0032, ..., -0.0250, -0.0186, 0.0119],
[-0.0042, 0.0047, -0.0134, ..., -0.0077, 0.0078, -0.0031],
[-0.0422, -0.0249, -0.0136, ..., -0.0295, 0.0302, -0.0123],
...,
[ 0.0076, -0.0002, -0.0001, ..., 0.0093, -0.0047, -0.0044],
[-0.0014, -0.0033, -0.0076, ..., -0.0109, -0.0012, -0.0215],
[-0.0232, 0.0025, 0.0060, ..., 0.0068, -0.0093, -0.0073]],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.self_attn.v_proj.lora_A.default.weight', torch.float32, False, tensor([[ 0.0090, 0.0004, -0.0076, ..., 0.0153, 0.0041, -0.0103],
[ 0.0138, 0.0015, -0.0053, ..., -0.0128, 0.0011, -0.0022],
[-0.0111, -0.0032, 0.0006, ..., -0.0077, 0.0128, 0.0151],
...,
[-0.0029, 0.0131, 0.0163, ..., -0.0044, -0.0083, -0.0115],
[ 0.0041, 0.0133, 0.0181, ..., 0.0027, 0.0083, 0.0050],
[ 0.0024, -0.0063, 0.0042, ..., -0.0021, -0.0047, 0.0007]]))
('base_model.model.base_model.model.model.layers.31.self_attn.v_proj.lora_B.default.weight', torch.float32, False, tensor([[-3.0396e-04, -1.8482e-04, -4.5843e-04, ..., -7.0217e-05,
-7.8356e-04, -3.6097e-04],
[-6.1859e-04, -1.8265e-04, 3.9717e-04, ..., -1.0677e-03,
-1.9114e-03, -1.1090e-03],
[-3.2717e-04, 5.8097e-04, 2.8924e-04, ..., 2.1774e-04,
2.1301e-04, -4.1886e-04],
...,
[-1.9359e-03, -1.4303e-03, -1.5492e-03, ..., -1.3788e-03,
-1.8295e-03, -2.1401e-03],
[-6.3756e-04, -5.9278e-04, -4.7388e-04, ..., -4.0911e-04,
-2.1048e-04, -5.5398e-04],
[-1.9948e-03, -2.3558e-03, -2.5491e-03, ..., -1.5711e-03,
-1.9010e-03, -2.4418e-03]]))
('base_model.model.base_model.model.model.layers.31.self_attn.o_proj.weight', torch.bfloat16, False, tensor([[ 0.0198, -0.0121, -0.0223, ..., 0.0117, -0.0031, 0.0131],
[ 0.0003, -0.0032, -0.0046, ..., 0.0013, -0.0013, 0.0123],
[-0.0073, 0.0129, -0.0085, ..., -0.0016, 0.0074, -0.0052],
...,
[ 0.0015, 0.0116, -0.0047, ..., -0.0004, -0.0016, 0.0125],
[-0.0167, 0.0030, 0.0166, ..., -0.0014, -0.0126, 0.0087],
[ 0.0172, -0.0017, 0.0156, ..., -0.0197, 0.0104, -0.0012]],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.mlp.gate_proj.weight', torch.bfloat16, False, tensor([[-0.0142, 0.0153, -0.0243, ..., 0.0134, 0.0041, 0.0069],
[-0.0006, 0.0083, 0.0076, ..., 0.0160, 0.0080, 0.0121],
[-0.0128, 0.0200, -0.0142, ..., 0.0060, -0.0074, -0.0006],
...,
[ 0.0054, -0.0041, 0.0251, ..., 0.0022, 0.0177, 0.0177],
[-0.0178, -0.0178, 0.0045, ..., 0.0131, -0.0084, -0.0108],
[-0.0269, -0.0266, -0.0234, ..., 0.0366, 0.0393, 0.0086]],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.mlp.up_proj.weight', torch.bfloat16, False, tensor([[-1.6357e-02, -2.5513e-02, -6.4087e-03, ..., 1.8555e-02,
-7.5684e-03, 9.8877e-03],
[ 6.4697e-03, 1.2436e-03, 1.2390e-02, ..., 7.2937e-03,
3.4668e-02, -3.9673e-03],
[ 4.4556e-03, 2.2583e-03, 5.8594e-03, ..., -8.6060e-03,
1.4465e-02, -2.1484e-02],
...,
[ 2.5757e-02, 1.9836e-03, 1.8677e-02, ..., 5.1880e-03,
1.0864e-02, 9.9659e-05],
[-3.5400e-03, 1.4221e-02, -8.6060e-03, ..., 6.6833e-03,
1.0071e-03, 1.2512e-02],
[ 9.5825e-03, -1.3489e-02, -1.3885e-03, ..., -4.4861e-03,
-3.0823e-03, -9.3384e-03]], dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.mlp.down_proj.weight', torch.bfloat16, False, tensor([[-0.0334, 0.0152, 0.0194, ..., 0.0025, 0.0114, 0.0013],
[-0.0050, -0.0064, 0.0026, ..., -0.0042, 0.0032, -0.0086],
[-0.0094, -0.0220, -0.0214, ..., 0.0029, -0.0057, -0.0084],
...,
[ 0.0125, 0.0017, -0.0042, ..., -0.0166, -0.0077, 0.0110],
[-0.0128, 0.0073, 0.0140, ..., -0.0193, 0.0125, 0.0109],
[-0.0074, -0.0139, 0.0104, ..., 0.0105, 0.0035, 0.0027]],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.input_layernorm.weight', torch.bfloat16, False, tensor([0.4629, 0.3984, 0.4434, ..., 0.4277, 0.3887, 0.2988],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.layers.31.post_attention_layernorm.weight', torch.bfloat16, False, tensor([0.5273, 0.4883, 0.5078, ..., 0.5273, 0.4863, 0.4238],
dtype=torch.bfloat16))
('base_model.model.base_model.model.model.norm.weight', torch.bfloat16, False, tensor([2.6562, 2.5781, 2.6094, ..., 2.5938, 2.2656, 2.5156],
dtype=torch.bfloat16))
('base_model.model.base_model.model.lm_head.weight', torch.bfloat16, False, tensor([[ 9.8267e-03, 1.7456e-02, 3.6926e-03, ..., 5.7983e-04,
-1.6968e-02, -1.0986e-02],
[-6.5613e-03, 1.1719e-02, 1.1536e-02, ..., -1.0193e-02,
9.3994e-03, -1.4496e-03],
[ 1.4160e-02, 9.4604e-03, 7.4463e-03, ..., -1.9379e-03,
-5.7678e-03, -1.4771e-02],
...,
[-3.7231e-03, -1.3065e-04, 7.5684e-03, ..., 9.6798e-05,
7.5989e-03, 6.0425e-03],
[-3.7231e-03, -1.3065e-04, 7.5684e-03, ..., 9.6798e-05,
7.5989e-03, 6.0425e-03],
[-3.7231e-03, -1.3065e-04, 7.5684e-03, ..., 9.6798e-05,
7.5989e-03, 6.0425e-03]], dtype=torch.bfloat16))
trainable params: 0 || all params: 8039698432 || trainable%: 0.0
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
128009
128000
128009
198
882
78191
{'content': '类型#裙*版型#宽松*版型#显瘦*颜色#黑色*图案#撞色*裙型#直筒裙*裙款式#拼接'}
tensor([[128000, 128006, 9125, 128007, 271, 2675, 527, 264, 11190,
18328, 11, 220, 15225, 11883, 99337, 33014, 108891, 113925,
13, 128009, 128006, 882, 128007, 271, 33005, 2, 70892,
247, 9, 41401, 25287, 2, 118188, 104500, 9, 41401,
25287, 2, 105593, 114431, 99, 9, 124510, 39135, 2,
57752, 39135, 9, 29129, 81742, 2, 58843, 252, 39135,
9, 70892, 247, 25287, 2, 74245, 127946, 70892, 247,
9, 70892, 247, 69253, 29430, 2, 125973, 30177, 128009,
128006, 78191, 128007, 271]])
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裙*版型#宽松*版型#显瘦*颜色#黑色*图案#撞色*裙型#直筒裙*裙款式#拼接<|eot_id|><|start_header_id|>assistant<|end_header_id|>
根据你的描述，这是一件黑色撞色拼接直筒裙，裙型宽松，版型显瘦，裙款式拼接。<|eot_id|>
617.0838704109192
请输入:
类型#裤*材质#牛仔布*颜色#白色*风格#简约*图案#线条*裤长#短裤*裤型#阔腿裤*裤腰型#高腰*裤口#毛边
请稍等...
################################################################################################################################
{'content': '类型#裤*材质#牛仔布*颜色#白色*风格#简约*图案#线条*裤长#短裤*裤型#阔腿裤*裤腰型#高腰*裤口#毛边'}
tensor([[128000, 128006, 9125, 128007, 271, 2675, 527, 264, 11190,
18328, 11, 220, 15225, 11883, 99337, 33014, 108891, 113925,
13, 128009, 128006, 882, 128007, 271, 33005, 2, 70892,
97, 9, 103234, 103706, 2, 109022, 120248, 52927, 9,
124510, 39135, 2, 101828, 39135, 9, 103125, 35083, 2,
99337, 95337, 9, 29129, 81742, 2, 44368, 40089, 9,
70892, 97, 46961, 2, 106649, 70892, 97, 9, 70892,
97, 25287, 2, 33443, 242, 122072, 70892, 97, 9,
70892, 97, 115519, 25287, 2, 45736, 115519, 9, 70892,
97, 40526, 2, 105811, 103377, 128009, 128006, 78191, 128007,
271]])
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裤*材质#牛仔布*颜色#白色*风格#简约*图案#线条*裤长#短裤*裤型#阔腿裤*裤腰型#高腰*裤口#毛边<|eot_id|><|start_header_id|>assistant<|end_header_id|>
根据您的描述，这是一双简约的短裤，使用牛仔布，白色为主色，线条图案，阔腿裤型，高腰，毛边裤口。<|eot_id|>
推理日志-advgen(SFT), 修改prompt微调, 指令遵循得不是太好, 感觉还是中文预训练语料太少了吧
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请根据输入生成创意广告, 用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裙*版型#宽松*版型#显瘦*颜色#黑色*图案#撞色*裙型#直筒裙*裙款式#拼接<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Here's a creative advertisement based on your input:
**Headline:** 撞色撞焦点！黑色撞色拼接直筒裙
**Body:**
你准备好撞焦点了吗？我们的黑色撞色拼接直筒裙将让你在人群中脱颖而出！
宽松版型，显瘦效果，让你显得更加苗条迷人。撞色图案，黑色拼接，更加增加裙子的时尚感和吸引力。
这不是一件裙子，而是一件生活方式。穿上我们的黑色撞色拼接直筒裙，你将成为焦点，吸引所有人的目光。
**Call to Action:** 快来试穿我们的黑色撞色拼接直筒裙，体验一下撞焦点的感觉！
**Visuals:** A stunning image of a model wearing the black and color-blocked straight-cut skirt, posing confidently with a bold expression. The skirt is designed to accentuate her curves, and the color-blocked pattern adds a touch of edginess to the overall look. The background is a dark, sleek cityscape, emphasizing the
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请根据输入生成创意广告, 用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裤*材质#牛仔布*颜色#白色*风格#简约*图案#线条*裤长#短裤*裤型#阔腿裤*裤腰型<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Here's a creative ad for the specified type of pants:
**Title:** 时尚简约，阔腿裤，白色牛仔布短裤
**Image:** A stylish photo of a person wearing the white cowboy-style short pants with a simple and minimalist design. The pants have a wide leg and a high waist, with a subtle stitching pattern along the seams.
**Headline:** 简约 yet 时尚，阔腿裤白色牛仔布短裤，让你展现自己的个性！
**Body:** 不需要很多花哨的设计，阔腿裤白色牛仔布短裤就足以让你展现自己的个性！这款简约的短裤，以牛仔布为材料，白色为主色调，线条简洁，给人一种简洁 yet 时尚的感觉。无论是街头、工作或休闲，这款短裤都能满足你的需求，让你展现自己的个性！
**Call-to-Action:** 快来抢购这款简约 yet 时尚的短裤，展现自己的个性！
**Target Audience:** Young adults who value simplicity and individuality
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请根据输入生成创意广告, 用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裤*材质#牛仔布*颜色#白色*风格#简约*图案#线条*裤长#短裤*裤型#阔腿裤*裤腰型#高腰*裤口#毛边<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Here's a creative advertisement for the product:
**Title:** 升级你的夏日风采！【简约牛仔短裤】
**Image:** A stylish model wearing the white cowhide short pants with a high waist and wide legs, posing in a casual yet chic way. The pants have a subtle texture and a raw edge hem, adding a touch of edginess to the overall look.
**Headline:** 升级你的夏日风采！简单 yet chic，简约牛仔短裤让你夏日更加精彩！
**Body Text:** 夏日到了，时尚也到了！简约牛仔短裤是夏日的必备！高腰设计，阔腿裤造型，毛边裤口，简约牛仔短裤让你夏日更加精彩！不仅舒适还非常时尚，简约牛仔短裤是你的夏日必备！
**Call-to-Action:** 快来获取你的简约牛仔短裤，升级你的夏日风采！
This advertisement highlights the key features of the product, such as the high waist, wide legs, and raw edge hem, while also emphasizing its comfort and stylishness
推理日志-advgen(原始Instruct)
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裙*材质#天丝*裙长#连衣裙*裙袖长#长袖<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Based on your input, I understand that you are looking for information about a dress with the following specifications:
* Type: 连衣裙 (a-line dress)
* Material: 天丝 (silk)
* Length: 不指定 (no specific length mentioned, but since it's a 连衣裙, it's likely to be knee-length or longer)
* Sleeve length: 长袖 (long sleeves)
Here's a summary of your requirements:
* A-line dress made of silk with long sleeves and a knee-length or longer hem.
If you have any further questions or would like to know more about this type of dress, feel free to ask! ?<|eot_id|>
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裙*版型#宽松*版型#显瘦*颜色#黑色*图案#撞色*裙型#直筒裙*裙款式#拼接<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Based on your input, I can help you with the following information:
* 类型：裙 (type: skirt)
* 版型：宽松 (style: loose-fitting)
* 版型：显瘦 (style: slimming)
* 颜色：黑色 (color: black)
* 图案：撞色 (pattern: contrast color)
* 裙型：直筒裙 (skirt type: straight skirt)
* 裙款式：拼接 (skirt style: patchwork)
So, the overall description is: a black straight skirt with a loose-fitting and slimming design, featuring a contrast color pattern through patchwork.<|eot_id|>
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant, 请用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裤*材质#牛仔布*颜色#白色*风格#简约*图案#线条*裤长#短裤*裤型#阔腿裤*裤腰型#高腰*裤口#毛边<|eot_id|><|start_header_id|>assistant<|end_header_id|>
您所描述的裤子是：
* 材质：牛仔布（denim）
* 颜色：白色（white）
* 风格：简约（simple）
* 图案：线条（striped）
* 裤长：短裤（short）
* 裤型：阔腿裤（wide-leg）
* 裤腰型：高腰（high-waisted）
* 裤口：毛边（fringed）
总的来说，这是一双简约、白色的牛仔短裤，具有线条图案、阔腿设计和高腰裤口。<|eot_id|>
d|>
You are a helpful assistant, 请用简体中文回答.<|eot_id|><|start_header_id|>user<|end_header_id|>
类型#裤材质#牛仔布颜色#白色风格#简约图案#线条裤长#短裤裤型#阔腿裤裤腰型#高腰裤口#毛边<|eot_id|><|start_header_id|>assistant<|end_header_id|>
您所描述的裤子是：
材质：牛仔布（denim） 颜色：白色（white） 风格：简约（simple） 图案：线条（striped） 裤长：短裤（short） 裤型：阔腿裤（wide-leg） 裤腰型：高腰（high-waisted） 裤口：毛边（fringed）总的来说，这是一双简约、白色的牛仔短裤，具有线条图案、阔腿设计和高腰裤口。<|eot_id|>