add dataset stage and filter dataset when stage chosen in webui
This commit is contained in:
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1c702ad538
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c0e4d1e81b
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@ -1,23 +1,28 @@
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{
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"alpaca_en": {
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"file_name": "alpaca_data_en_52k.json",
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"file_sha1": "607f94a7f581341e59685aef32f531095232cf23"
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"file_sha1": "607f94a7f581341e59685aef32f531095232cf23",
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"stage": "sft"
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},
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"alpaca_zh": {
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"file_name": "alpaca_data_zh_51k.json",
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"file_sha1": "e655af3db557a4197f7b0cf92e1986b08fae6311"
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"file_sha1": "e655af3db557a4197f7b0cf92e1986b08fae6311",
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"stage": "sft"
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},
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"alpaca_gpt4_en": {
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"file_name": "alpaca_gpt4_data_en.json",
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"file_sha1": "647f4ad447bd993e4b6b6223d1be15208bab694a"
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"file_sha1": "647f4ad447bd993e4b6b6223d1be15208bab694a",
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"stage": "sft"
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},
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"alpaca_gpt4_zh": {
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"file_name": "alpaca_gpt4_data_zh.json",
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"file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845"
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"file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845",
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"stage": "sft"
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},
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"self_cognition": {
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"file_name": "self_cognition.json",
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"file_sha1": "6287a730ada924fc5d9eadc6d8f865e01b7a6f67"
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"file_sha1": "6287a730ada924fc5d9eadc6d8f865e01b7a6f67",
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"stage": "sft"
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},
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"oaast_sft": {
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"file_name": "oaast_sft.json",
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@ -27,7 +32,8 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"oaast_sft_zh": {
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"file_name": "oaast_sft_zh.json",
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@ -37,7 +43,8 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"sharegpt_zh": {
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"file_name": "sharegpt_zh_27k.json",
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@ -47,7 +54,8 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"lima": {
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"file_name": "lima.json",
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@ -57,7 +65,8 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"example": {
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"script_url": "example_dataset",
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@ -66,25 +75,32 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"guanaco": {
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"hf_hub_url": "JosephusCheung/GuanacoDataset"
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"hf_hub_url": "JosephusCheung/GuanacoDataset",
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"stage": "sft"
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},
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"belle_0.5m": {
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"hf_hub_url": "BelleGroup/train_0.5M_CN"
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"hf_hub_url": "BelleGroup/train_0.5M_CN",
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"stage": "sft"
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},
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"belle_1m": {
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"hf_hub_url": "BelleGroup/train_1M_CN"
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"hf_hub_url": "BelleGroup/train_1M_CN",
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"stage": "sft"
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},
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"belle_2m": {
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"hf_hub_url": "BelleGroup/train_2M_CN"
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"hf_hub_url": "BelleGroup/train_2M_CN",
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"stage": "sft"
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},
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"belle_dialog": {
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"hf_hub_url": "BelleGroup/generated_chat_0.4M"
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"hf_hub_url": "BelleGroup/generated_chat_0.4M",
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"stage": "sft"
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},
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"belle_math": {
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"hf_hub_url": "BelleGroup/school_math_0.25M"
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"hf_hub_url": "BelleGroup/school_math_0.25M",
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"stage": "sft"
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},
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"belle_multiturn": {
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"script_url": "belle_multiturn",
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@ -93,7 +109,8 @@
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"firefly": {
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"hf_hub_url": "YeungNLP/firefly-train-1.1M",
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@ -102,13 +119,16 @@
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"query": "",
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"response": "target",
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"history": ""
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}
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},
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"stage": "sft"
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},
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"codealpaca": {
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"hf_hub_url": "sahil2801/CodeAlpaca-20k"
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"hf_hub_url": "sahil2801/CodeAlpaca-20k",
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"stage": "sft"
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},
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"alpaca_cot": {
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"hf_hub_url": "QingyiSi/Alpaca-CoT"
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"hf_hub_url": "QingyiSi/Alpaca-CoT",
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"stage": "sft"
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},
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"webqa": {
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"hf_hub_url": "suolyer/webqa",
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@ -117,7 +137,8 @@
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"query": "",
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"response": "output",
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"history": ""
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}
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},
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"stage": "sft"
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},
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"ultra_chat": {
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"script_url": "ultra_chat",
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@ -126,18 +147,22 @@
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "sft"
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},
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"novel_tokens512_50k": {
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"hf_hub_url": "zxbsmk/webnovel_cn"
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"hf_hub_url": "zxbsmk/webnovel_cn",
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"stage": "sft"
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},
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"comparison_gpt4_en": {
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"file_name": "comparison_gpt4_data_en.json",
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"file_sha1": "96fa18313544e22444fe20eead7754b17da452ae"
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"file_sha1": "96fa18313544e22444fe20eead7754b17da452ae",
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"stage": "rm"
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},
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"comparison_gpt4_zh": {
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"file_name": "comparison_gpt4_data_zh.json",
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"file_sha1": "515b18ed497199131ddcc1af950345c11dc5c7fd"
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"file_sha1": "515b18ed497199131ddcc1af950345c11dc5c7fd",
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"stage": "rm"
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},
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"hh_rlhf_en": {
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"script_url": "hh_rlhf_en",
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@ -146,7 +171,8 @@
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "rm"
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},
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"oaast_rm": {
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"file_name": "oaast_rm.json",
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@ -156,7 +182,8 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "rm"
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},
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"oaast_rm_zh": {
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"file_name": "oaast_rm_zh.json",
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@ -166,7 +193,8 @@
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"stage": "rm"
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},
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"wiki_demo": {
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"file_name": "wiki_demo.txt",
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@ -176,7 +204,8 @@
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"query": "",
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"response": "",
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"history": ""
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}
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},
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"stage": "pt"
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},
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"refinedweb": {
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"hf_hub_url": "tiiuae/falcon-refinedweb",
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@ -185,7 +214,8 @@
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"query": "",
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"response": "",
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"history": ""
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}
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},
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"stage": "pt"
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},
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"starcoder": {
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"hf_hub_url": "bigcode/starcoderdata",
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@ -194,7 +224,8 @@
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"query": "",
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"response": "",
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"history": ""
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}
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},
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"stage": "pt"
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},
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"wikipedia_en": {
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"hf_hub_url": "olm/olm-wikipedia-20221220",
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@ -203,7 +234,8 @@
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"query": "",
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"response": "",
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"history": ""
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}
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},
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"stage": "pt"
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},
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"wikipedia_zh": {
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"hf_hub_url": "pleisto/wikipedia-cn-20230720-filtered",
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@ -212,6 +244,7 @@
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"query": "",
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"response": "",
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"history": ""
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}
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},
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"stage": "pt"
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}
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}
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@ -18,6 +18,14 @@ STAGES = [
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"Pre-Training"
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]
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DATASET_STAGE_MAP = {
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"SFT": "sft",
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"Pre-Training": "pt",
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"Reward Modeling": "rm",
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"PPO": "sft",
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"DPO": "rm"
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}
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SUPPORTED_MODELS = {
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"LLaMA-7B": "huggyllama/llama-7b",
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"LLaMA-13B": "huggyllama/llama-13b",
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@ -6,7 +6,7 @@ import gradio as gr
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from peft.utils import WEIGHTS_NAME as PEFT_WEIGHTS_NAME
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from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
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from llmtuner.extras.constants import DEFAULT_TEMPLATE, SUPPORTED_MODELS
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from llmtuner.extras.constants import DEFAULT_TEMPLATE, SUPPORTED_MODELS, DATASET_STAGE_MAP
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DEFAULT_CACHE_DIR = "cache"
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@ -78,6 +78,11 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Any]:
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return {}
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def list_dataset(dataset_dir: Optional[str] = None) -> Dict[str, Any]:
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def list_dataset(dataset_dir: Optional[str] = None, stage: Optional[str] = None) -> Dict[str, Any]:
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dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
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return gr.update(value=[], choices=list(dataset_info.keys()))
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if stage:
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dataset_stage = DATASET_STAGE_MAP[stage]
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dataset_info = {key: value for key, value in dataset_info.items()
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if ("stage" not in value) or value["stage"] == dataset_stage}
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return gr.update(value=[], choices=list(dataset_info.keys()))
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@ -22,7 +22,8 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
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preview_box, preview_count, preview_samples, close_btn = create_preview_box()
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dataset_dir.change(list_dataset, [dataset_dir], [dataset])
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training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset])
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dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset])
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dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn])
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data_preview_btn.click(
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get_preview,
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