commit b78ff4f4871ebf7f74189d73f82c2d93f97b9d84 Author: ModelHub XC Date: Sun Jul 5 18:55:18 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: skyai798/saferlhf_ultra_sft Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..984cd33 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx 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-text diff --git a/README.md b/README.md new file mode 100644 index 0000000..9ade5e1 --- /dev/null +++ b/README.md @@ -0,0 +1,60 @@ +--- +library_name: transformers +license: other +base_model: meta-llama/Llama-3.1-8B-Instruct +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: saferlhf-ultra-lr5e-6-ep3 + results: [] +--- + + + +# saferlhf-ultra-lr5e-6-ep3 + +This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the saferlhf_ultra dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 5e-06 +- train_batch_size: 4 +- eval_batch_size: 8 +- seed: 42 +- distributed_type: multi-GPU +- num_devices: 8 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 128 +- total_eval_batch_size: 64 +- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 +- lr_scheduler_type: cosine +- num_epochs: 3.0 + +### Training results + + + +### Framework versions + +- Transformers 4.45.2 +- Pytorch 2.3.0+cu121 +- Datasets 2.21.0 +- Tokenizers 0.20.1 diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..779eb05 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 2.9952, + "total_flos": 80495178678272.0, + "train_loss": 0.9764440487592648, + "train_runtime": 2345.6045, + "train_samples_per_second": 25.58, + "train_steps_per_second": 0.2 +} \ No newline at end of file diff --git a/checkpoint-468/config.json b/checkpoint-468/config.json new file mode 100644 index 0000000..aa3024d --- /dev/null +++ b/checkpoint-468/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct", + "architectures": [ + "LlamaForCausalLM" + ], + 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"normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128258": { + "content": "<|Output|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128259": { + "content": "<|/Output|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|Reasoning_step|>", + "<|/Reasoning_step|>", + "<|Output|>", + "<|/Output|>" + ], + "bos_token": "<|begin_of_text|>", + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- 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builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|eot_id|>", + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|eot_id|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "PreTrainedTokenizerFast" +} diff --git a/train_results.json b/train_results.json new file mode 100644 index 0000000..779eb05 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 2.9952, + "total_flos": 80495178678272.0, + "train_loss": 0.9764440487592648, + "train_runtime": 2345.6045, + "train_samples_per_second": 25.58, + "train_steps_per_second": 0.2 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..2768a8b --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,16 @@ +{"current_steps": 30, "total_steps": 468, "loss": 1.2661, "learning_rate": 4.94947663010567e-06, "epoch": 0.192, "percentage": 6.41, "elapsed_time": "0:02:15", "remaining_time": "0:33:00"} +{"current_steps": 60, "total_steps": 468, "loss": 1.1198, "learning_rate": 4.799948609147061e-06, "epoch": 0.384, "percentage": 12.82, "elapsed_time": "0:04:24", "remaining_time": "0:30:00"} +{"current_steps": 90, "total_steps": 468, "loss": 1.1018, "learning_rate": 4.5574596647341414e-06, "epoch": 0.576, "percentage": 19.23, "elapsed_time": "0:06:37", "remaining_time": "0:27:49"} +{"current_steps": 120, "total_steps": 468, "loss": 1.1007, "learning_rate": 4.231810883773999e-06, "epoch": 0.768, "percentage": 25.64, "elapsed_time": "0:09:50", "remaining_time": "0:28:31"} +{"current_steps": 150, "total_steps": 468, "loss": 1.0833, "learning_rate": 3.836164565319503e-06, "epoch": 0.96, "percentage": 32.05, "elapsed_time": "0:11:58", "remaining_time": "0:25:23"} +{"current_steps": 180, "total_steps": 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"learning_rate": 9.981443394050525e-07, "epoch": 2.112, "percentage": 70.51, "elapsed_time": "0:27:01", "remaining_time": "0:11:18"} +{"current_steps": 360, "total_steps": 468, "loss": 0.8429, "learning_rate": 6.28723129572247e-07, "epoch": 2.304, "percentage": 76.92, "elapsed_time": "0:29:11", "remaining_time": "0:08:45"} +{"current_steps": 390, "total_steps": 468, "loss": 0.8478, "learning_rate": 3.3493649053890325e-07, "epoch": 2.496, "percentage": 83.33, "elapsed_time": "0:31:24", "remaining_time": "0:06:16"} +{"current_steps": 420, "total_steps": 468, "loss": 0.8463, "learning_rate": 1.286588951321363e-07, "epoch": 2.6879999999999997, "percentage": 89.74, "elapsed_time": "0:34:39", "remaining_time": "0:03:57"} +{"current_steps": 450, "total_steps": 468, "loss": 0.8508, "learning_rate": 1.822781475486507e-08, "epoch": 2.88, "percentage": 96.15, "elapsed_time": "0:36:48", "remaining_time": "0:01:28"} +{"current_steps": 468, "total_steps": 468, "epoch": 2.9952, "percentage": 100.0, "elapsed_time": "0:39:05", "remaining_time": "0:00:00"} diff --git a/trainer_state.json b/trainer_state.json new file mode 100644 index 0000000..d7db2e2 --- /dev/null +++ b/trainer_state.json @@ -0,0 +1,147 @@ +{ + "best_metric": null, + "best_model_checkpoint": null, + "epoch": 2.9952, + "eval_steps": 500, + "global_step": 468, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 0.192, + "grad_norm": 1.5544959976587758, + "learning_rate": 4.94947663010567e-06, + "loss": 1.2661, + "step": 30 + }, + { + "epoch": 0.384, + "grad_norm": 1.58877277838815, + "learning_rate": 4.799948609147061e-06, + "loss": 1.1198, + "step": 60 + }, + { + "epoch": 0.576, + "grad_norm": 1.4076735500904218, + "learning_rate": 4.5574596647341414e-06, + "loss": 1.1018, + "step": 90 + }, + { + "epoch": 0.768, + "grad_norm": 1.4296012725480793, + "learning_rate": 4.231810883773999e-06, + "loss": 1.1007, + "step": 120 + }, + { + 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1.3575000714838341, + "learning_rate": 6.28723129572247e-07, + "loss": 0.8429, + "step": 360 + }, + { + "epoch": 2.496, + "grad_norm": 1.3763689831257202, + "learning_rate": 3.3493649053890325e-07, + "loss": 0.8478, + "step": 390 + }, + { + "epoch": 2.6879999999999997, + "grad_norm": 1.4156917358264154, + "learning_rate": 1.286588951321363e-07, + "loss": 0.8463, + "step": 420 + }, + { + "epoch": 2.88, + "grad_norm": 1.3846493525562265, + "learning_rate": 1.822781475486507e-08, + "loss": 0.8508, + "step": 450 + }, + { + "epoch": 2.9952, + "step": 468, + "total_flos": 80495178678272.0, + "train_loss": 0.9764440487592648, + "train_runtime": 2345.6045, + "train_samples_per_second": 25.58, + "train_steps_per_second": 0.2 + } + ], + "logging_steps": 30, + "max_steps": 468, + "num_input_tokens_seen": 0, + "num_train_epochs": 3, + "save_steps": 100, + "stateful_callbacks": { + "TrainerControl": { + "args": { + "should_epoch_stop": false, + "should_evaluate": false, + "should_log": false, + "should_save": true, + "should_training_stop": true + }, + "attributes": {} + } + }, + "total_flos": 80495178678272.0, + "train_batch_size": 4, + "trial_name": null, + "trial_params": null +} diff --git a/training_args.bin b/training_args.bin new file mode 100644 index 0000000..ec3a4c2 --- /dev/null +++ b/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b7aad13f8e0fc9372d00a3e65deb942eac1086b198d24f9092c55fa26db0a9a +size 7160 diff --git a/training_loss.png b/training_loss.png new file mode 100644 index 0000000..35f04b9 Binary files /dev/null and b/training_loss.png differ