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README.md
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@@ -28,6 +28,22 @@ The overall performance of chatglm-6b-belle-zh-lora on QA **test**:
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在中文开放测试集中的表现优异,继承了两方面的优势:1)微调训练的底座是Ziya-LLaMA-13B模型,是较强的中英文底座模型,2)微调使用的是高质量240万条中英文医疗指令数据集,和多种通用指令数据集,微调后的模型在医疗行业答复能力达到领先水平,在通用问题上的答复能力不弱于LLaMA-13B。
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## Usage
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本项目开源在 github repo:
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- [shibing624/textgen](https://github.com/shibing624/textgen)
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如果需要训练ChatGLM/LLAMA/BLOOM模型,请参考[https://github.com/shibing624/textgen](https://github.com/shibing624/textgen)
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## Training details
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training args:
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```json
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{"per_device_train_batch_size": 8, "per_device_eval_batch_size": 8, "per_gpu_train_batch_size": null, "per_gpu_eval_batch_size": null, "gradient_accumulation_steps": 1, "eval_accumulation_steps": null, "eval_delay": 0, "learning_rate": 2e-05, "weight_decay": 0.0, "adam_beta1": 0.9, "adam_beta2": 0.999, "adam_epsilon": 1e-08, "max_grad_norm": 1.0, "num_train_epochs": 10.0, "max_steps": -1, "lr_scheduler_type": "linear", "warmup_ratio": 0.0, "warmup_steps": 50, "log_level": "passive", "log_level_replica": "warning", "log_on_each_node": true, "logging_dir": "outputs-ziya-llama-13b-sft-med-v2/logs", "logging_strategy": "steps", "logging_first_step": false, "logging_steps": 50, "logging_nan_inf_filter": true, "save_strategy": "steps", "save_steps": 50, "save_total_limit": 3, "save_safetensors": false, "save_on_each_node": false, "no_cuda": false, "use_mps_device": false, "seed": 42, "data_seed": null, "jit_mode_eval": false, "use_ipex": false, "bf16": false, "fp16": true, "fp16_opt_level": "O1", "half_precision_backend": "cuda_amp", "bf16_full_eval": false, "fp16_full_eval": false, "tf32": null, "local_rank": 0, "xpu_backend": null, "tpu_num_cores": null, "tpu_metrics_debug": false, "debug": [], "dataloader_drop_last": false, "eval_steps": 50, "dataloader_num_workers": 0, "past_index": -1, "run_name": "outputs-ziya-llama-13b-sft-med-v2", "disable_tqdm": false, "remove_unused_columns": false, "label_names": null, "load_best_model_at_end": true, "metric_for_best_model": "loss", "greater_is_better": false, "ignore_data_skip": false, "sharded_ddp": [], "fsdp": [], "fsdp_min_num_params": 0, "fsdp_config": { "fsdp_min_num_params": 0, "xla": false, "xla_fsdp_grad_ckpt": false }, "fsdp_transformer_layer_cls_to_wrap": null, "deepspeed": null, "label_smoothing_factor": 0.0, "optim": "adamw_torch", "optim_args": null, "adafactor": false, "group_by_length": false, "length_column_name": "length", "report_to": [ "tensorboard" ], "ddp_find_unused_parameters": false, "ddp_bucket_cap_mb": null, "dataloader_pin_memory": true, "skip_memory_metrics": true, "use_legacy_prediction_loop": false, "push_to_hub": false, "resume_from_checkpoint": null, "hub_model_id": null, "hub_strategy": "every_save", "hub_token": "<hub_token>", "hub_private_repo": false, "gradient_checkpointing": false, "include_inputs_for_metrics": false, "fp16_backend": "auto", "push_to_hub_model_id": null, "push_to_hub_organization": null, "push_to_hub_token": "<push_to_hub_token>", "mp_parameters": "", "auto_find_batch_size": false, "full_determinism": false, "torchdynamo": null, "ray_scope": "last", "ddp_timeout": 1800, "torch_compile": false, "torch_compile_backend": null, "torch_compile_mode": null }
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```
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train log:
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![](https://huggingface.co/shibing624/ziya-llama-13b-medical-lora/blob/main/trainloss.png)
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evaluate log:
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![](https://huggingface.co/shibing624/ziya-llama-13b-medical-lora/blob/main/evalloss.png)
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## Citation
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在中文开放测试集中的表现优异,继承了两方面的优势:1)微调训练的底座是Ziya-LLaMA-13B模型,是较强的中英文底座模型,2)微调使用的是高质量240万条中英文医疗指令数据集,和多种通用指令数据集,微调后的模型在医疗行业答复能力达到领先水平,在通用问题上的答复能力不弱于LLaMA-13B。
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## Training details
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training args:
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```json
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{"per_device_train_batch_size": 8, "per_device_eval_batch_size": 8, "per_gpu_train_batch_size": null, "per_gpu_eval_batch_size": null, "gradient_accumulation_steps": 1, "eval_accumulation_steps": null, "eval_delay": 0, "learning_rate": 2e-05, "weight_decay": 0.0, "adam_beta1": 0.9, "adam_beta2": 0.999, "adam_epsilon": 1e-08, "max_grad_norm": 1.0, "num_train_epochs": 10.0, "max_steps": -1, "lr_scheduler_type": "linear", "warmup_ratio": 0.0, "warmup_steps": 50, "log_level": "passive", "log_level_replica": "warning", "log_on_each_node": true, "logging_dir": "outputs-ziya-llama-13b-sft-med-v2/logs", "logging_strategy": "steps", "logging_first_step": false, "logging_steps": 50, "logging_nan_inf_filter": true, "save_strategy": "steps", "save_steps": 50, "save_total_limit": 3, "save_safetensors": false, "save_on_each_node": false, "no_cuda": false, "use_mps_device": false, "seed": 42, "data_seed": null, "jit_mode_eval": false, "use_ipex": false, "bf16": false, "fp16": true, "fp16_opt_level": "O1", "half_precision_backend": "cuda_amp", "bf16_full_eval": false, "fp16_full_eval": false, "tf32": null, "local_rank": 0, "xpu_backend": null, "tpu_num_cores": null, "tpu_metrics_debug": false, "debug": [], "dataloader_drop_last": false, "eval_steps": 50, "dataloader_num_workers": 0, "past_index": -1, "run_name": "outputs-ziya-llama-13b-sft-med-v2", "disable_tqdm": false, "remove_unused_columns": false, "label_names": null, "load_best_model_at_end": true, "metric_for_best_model": "loss", "greater_is_better": false, "ignore_data_skip": false, "sharded_ddp": [], "fsdp": [], "fsdp_min_num_params": 0, "fsdp_config": { "fsdp_min_num_params": 0, "xla": false, "xla_fsdp_grad_ckpt": false }, "fsdp_transformer_layer_cls_to_wrap": null, "deepspeed": null, "label_smoothing_factor": 0.0, "optim": "adamw_torch", "optim_args": null, "adafactor": false, "group_by_length": false, "length_column_name": "length", "report_to": [ "tensorboard" ], "ddp_find_unused_parameters": false, "ddp_bucket_cap_mb": null, "dataloader_pin_memory": true, "skip_memory_metrics": true, "use_legacy_prediction_loop": false, "push_to_hub": false, "resume_from_checkpoint": null, "hub_model_id": null, "hub_strategy": "every_save", "hub_token": "<hub_token>", "hub_private_repo": false, "gradient_checkpointing": false, "include_inputs_for_metrics": false, "fp16_backend": "auto", "push_to_hub_model_id": null, "push_to_hub_organization": null, "push_to_hub_token": "<push_to_hub_token>", "mp_parameters": "", "auto_find_batch_size": false, "full_determinism": false, "torchdynamo": null, "ray_scope": "last", "ddp_timeout": 1800, "torch_compile": false, "torch_compile_backend": null, "torch_compile_mode": null }
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```
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train log:
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<img src="https://huggingface.co/shibing624/ziya-llama-13b-medical-lora/blob/main/trainloss.png" alt="trainloss">
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evaluate log:
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<img src="https://huggingface.co/shibing624/ziya-llama-13b-medical-lora/blob/main/evalloss.png" alt="trainloss">
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## Usage
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本项目开源在 github repo:
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- [shibing624/textgen](https://github.com/shibing624/textgen)
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如果需要训练ChatGLM/LLAMA/BLOOM模型,请参考[https://github.com/shibing624/textgen](https://github.com/shibing624/textgen)
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## Citation
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