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tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dialogsum_10.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dialogsum_20.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dialogsum_30.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dialogsum_40.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dialogsum_50.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dolly_10.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dolly_20.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dolly_30.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dolly_40.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dolly_50.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_self-inst_10.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_self-inst_20.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_self-inst_30.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_self-inst_40.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_self-inst_50.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_sinst_11__10.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_sinst_11__20.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_sinst_11__30.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_sinst_11__40.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_sinst_11__50.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_vicuna_10.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_vicuna_20.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_vicuna_30.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_vicuna_40.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_vicuna_50.jsonl +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/args.json +1 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/README.md +202 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/adapter_config.json +34 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/adapter_model.bin +3 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/hidden_states_projector.pt +3 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/projector.pt +3 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/special_tokens_map.json +24 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/tokenizer.json +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648/tokenizer_config.json +43 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch7_step10003_loss2.3743_rougel29.8116/README.md +202 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch7_step10003_loss2.3743_rougel29.8116/adapter_config.json +34 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch7_step10003_loss2.3743_rougel29.8116/adapter_model.bin +3 -0
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tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch7_step10003_loss2.3743_rougel29.8116/special_tokens_map.json +24 -0
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tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch7_step10003_loss2.3743_rougel29.8116/tokenizer_config.json +43 -0
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tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch8_step11432_loss2.5364_rougel29.8010/adapter_config.json +34 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch8_step11432_loss2.5364_rougel29.8010/adapter_model.bin +3 -0
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tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch8_step11432_loss2.5364_rougel29.8010/projector.pt +3 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch8_step11432_loss2.5364_rougel29.8010/special_tokens_map.json +24 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch8_step11432_loss2.5364_rougel29.8010/tokenizer.json +0 -0
tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch8_step11432_loss2.5364_rougel29.8010/tokenizer_config.json +43 -0

tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/answers_dialogsum_10.jsonl ADDED Viewed

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+ {"model_path": "/workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T", "ckpt_name": null, "model_type": "tinyllama", "teacher_model_type": null, "n_gpu": 1, "n_nodes": 1, "teacher_model_path": null, "teacher_model_fp16": false, "model_parallel": false, "model_parallel_size": null, "no_value": false, "dropout_path_rate": null, "fp32": false, "model_dtype": "fp16", "M_global_path": null, "embedding_projection_path": null, "task": "eval_main", "do_train": false, "do_valid": false, "do_eval": true, "base_path": "/workspace/DSKD", "load": null, "save_dir": "/workspace/DSKD/outputs/tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001", "log_interval": 10, "save_interval": 1000, "eval_interval": 1000, "local_rank": 0, "save_additional_suffix": "", "save_rollout": false, "eb_sample_times": 3, "keep_best_n_checkpoints": 3, "criterion": "cross_entropy", "eval_tqdm": false, "report_logits": false, "only_save_projector": false, "debug": false, "data_dir": "/workspace/DSKD/data/dolly", "processed_data_dir": null, "force_process": false, "force_process_demo": false, "data_process_workers": -1, "train_num": -1, "train_ratio": 1, "dev_num": -1, "dev_ratio": 1, "gen_num": -1, "data_names": "dolly", "prompt_type": null, "num_workers": 0, "max_prompt_length": 256, "min_prompt_length": 128, "json_data": true, "bin_data": false, "txt_data": false, "prompt_data_dir": null, "pretrain_data_dir": null, "eval_ppl": false, "eval_rw": false, "eval_gen": false, "only_prompt": false, "batch_size": 32, "eval_batch_size": 16, "clip_grad": 1.0, "total_iters": null, "train_iters_per_epoch": -1, "max_length": 512, "seed": 10, "seed_order": 42, "seed_data": 42, "seed_ppo": 42, "seed_lm": 7, "num_epochs": null, "training_epochs": 10000, "gradient_accumulation_steps": 1, "gradient_checkpointing": false, "attn_dtype": null, "lr": null, "lr_min": 1e-07, "weight_decay": 0.01, "loss_scale": 65536, "kd_rate": 0.5, "kd_temperature": 1.0, "wctkd_alpha": 0.5, "wctkd_beta": 0.5, "wctkd_gamma": 0.5, "wctkd_hidden_gamma": 0.5, "wctkd_top_k": 8, "kd_objective": "forward_kl", "teacher_temperature": 1.0, "label_smoothing": 0.0, "adaptive_kl_alpha": 0.5, "skew_lambda": 0.1, "warmup_iters": 0, "lr_decay_iters": null, "lr_decay_style": "noam", "scheduler_name": "constant_trm", "top_k": 0, "top_p": 1.0, "do_sample": true, "no_repeat_ngram_size": 6, "repetition_penalty": null, "num_beams": 1, "temperature": 1.0, "eval_gen_repeat_times": 3, "peft": "lora", "peft_lora_r": 16, "peft_lora_alpha": 64, "peft_lora_dropout": 0.1, "peft_name": null, "peft_path": "/workspace/DSKD/outputs/tinyllama/tinyllama-1.1b-3T/wctkd/criterion=wctkd__forward_kl-lora-rank=256-alpha=8-dropout=0.1-bf16__teacher=mistral__kd^rate=0.5__kd^temp=2.0__wctkd^alpha=0.5__wctkd^beta=0.2__wctkd^gamma=0.3__wctkd^hidden_gamma=0.5__wctkd^top_k=8__epoch=10__bsz=4x2x1=8__lr=0.001/epoch10_step14290_loss2.7041_rougel29.7648", "teacher_peft_name": null, "teacher_peft_path": null, "deepspeed": true, "deepspeed_config": "/workspace/DSKD/configs/deepspeed/ds_config_bf16.json", "deepscale": false, "deepscale_config": null, "projector_config_path": null, "projector_path": null, "projector_lr": 0.001, "pretrained_projector": null, "pretrained_projector_lr": 0.001, "vocab_alignment_path": null, "teacher_to_student_token_mapping": null, "teacher_to_student_id_mapping": null, "student_to_teacher_token_mapping": null, "student_to_teacher_id_mapping": null, "rank": 0, "world_size": 1}

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+---
+base_model: /workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T
+library_name: peft
+---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
+## Model Details
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[More Information Needed]
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+[More Information Needed]
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+[More Information Needed]
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+[More Information Needed]
+### Recommendations
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+[More Information Needed]
+## Training Details
+### Training Data
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[More Information Needed]
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset Card if possible. -->
+[More Information Needed]
+#### Factors
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+[More Information Needed]
+#### Metrics
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+[More Information Needed]
+### Results
+[More Information Needed]
+#### Summary
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
+## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.15.1

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
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+  "loftq_config": {},
+  "lora_alpha": 8,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 256,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

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+{
+  "add_bos_token": true,
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+  "added_tokens_decoder": {
+    "0": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

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+---
+base_model: /workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T
+library_name: peft
+---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
+## Model Details
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[More Information Needed]
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+[More Information Needed]
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+[More Information Needed]
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+[More Information Needed]
+### Recommendations
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+[More Information Needed]
+## Training Details
+### Training Data
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[More Information Needed]
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset Card if possible. -->
+[More Information Needed]
+#### Factors
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+[More Information Needed]
+#### Metrics
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+[More Information Needed]
+### Results
+[More Information Needed]
+#### Summary
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
+## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.15.1

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+{
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+  "base_model_name_or_path": "/workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T",
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+  "lora_dropout": 0.1,
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+    "v_proj"
+  ],
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+  "use_dora": false,
+  "use_rslora": false
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+---
+base_model: /workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T
+library_name: peft
+---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
+## Model Details
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[More Information Needed]
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+[More Information Needed]
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+[More Information Needed]
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+[More Information Needed]
+### Recommendations
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+[More Information Needed]
+## Training Details
+### Training Data
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[More Information Needed]
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset Card if possible. -->
+[More Information Needed]
+#### Factors
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+[More Information Needed]
+#### Metrics
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+[More Information Needed]
+### Results
+[More Information Needed]
+#### Summary
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
+## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.15.1

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/DSKD/model_hub/tinyllama/tinyllama-1.1b-3T",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
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+  "inference_mode": true,
+  "init_lora_weights": true,
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+  "loftq_config": {},
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+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
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+  "target_modules": [
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+  ],
+  "task_type": "CAUSAL_LM",
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+  "use_dora": false,
+  "use_rslora": false
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