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Model: W-61/qwen3-8b-base-sft-hh-harmless-8xh200 Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen3-8B-Base
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tags:
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- alignment-handbook
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- generated_from_trainer
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datasets:
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- Anthropic/hh-rlhf
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model-index:
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- name: qwen3-8b-base-sft-hh-harmless-8xh200-20260414-192602-232981
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# qwen3-8b-base-sft-hh-harmless-8xh200-20260414-192602-232981
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This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on the Anthropic/hh-rlhf dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6259
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 128
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.6122 | 0.9259 | 100 | 1.6259 |
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### Framework versions
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- Transformers 4.51.0
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.21.4
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all_results.json
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config.json
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"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 2048,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
9
train_results.json
Normal file
9
train_results.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"epoch": 1.0,
|
||||
"total_flos": 4.017624413372416e+16,
|
||||
"train_loss": 1.907559284457454,
|
||||
"train_runtime": 456.0106,
|
||||
"train_samples": 42336,
|
||||
"train_samples_per_second": 30.304,
|
||||
"train_steps_per_second": 0.237
|
||||
}
|
||||
128
trainer_state.json
Normal file
128
trainer_state.json
Normal file
@@ -0,0 +1,128 @@
|
||||
{
|
||||
"best_global_step": null,
|
||||
"best_metric": null,
|
||||
"best_model_checkpoint": null,
|
||||
"epoch": 1.0,
|
||||
"eval_steps": 100,
|
||||
"global_step": 108,
|
||||
"is_hyper_param_search": false,
|
||||
"is_local_process_zero": true,
|
||||
"is_world_process_zero": true,
|
||||
"log_history": [
|
||||
{
|
||||
"epoch": 0.009259259259259259,
|
||||
"grad_norm": 17.74639892578125,
|
||||
"learning_rate": 0.0,
|
||||
"loss": 3.4363,
|
||||
"step": 1
|
||||
},
|
||||
{
|
||||
"epoch": 0.09259259259259259,
|
||||
"grad_norm": 5.17479944229126,
|
||||
"learning_rate": 1.6363636363636366e-05,
|
||||
"loss": 3.0187,
|
||||
"step": 10
|
||||
},
|
||||
{
|
||||
"epoch": 0.18518518518518517,
|
||||
"grad_norm": 9.653449058532715,
|
||||
"learning_rate": 1.9666208503126115e-05,
|
||||
"loss": 2.3473,
|
||||
"step": 20
|
||||
},
|
||||
{
|
||||
"epoch": 0.2777777777777778,
|
||||
"grad_norm": 8.364947319030762,
|
||||
"learning_rate": 1.8348280530292712e-05,
|
||||
"loss": 2.076,
|
||||
"step": 30
|
||||
},
|
||||
{
|
||||
"epoch": 0.37037037037037035,
|
||||
"grad_norm": 4.204029560089111,
|
||||
"learning_rate": 1.6162284656009276e-05,
|
||||
"loss": 1.8518,
|
||||
"step": 40
|
||||
},
|
||||
{
|
||||
"epoch": 0.46296296296296297,
|
||||
"grad_norm": 2.5657479763031006,
|
||||
"learning_rate": 1.3335524316508208e-05,
|
||||
"loss": 1.743,
|
||||
"step": 50
|
||||
},
|
||||
{
|
||||
"epoch": 0.5555555555555556,
|
||||
"grad_norm": 2.2198078632354736,
|
||||
"learning_rate": 1.0161930688025018e-05,
|
||||
"loss": 1.6929,
|
||||
"step": 60
|
||||
},
|
||||
{
|
||||
"epoch": 0.6481481481481481,
|
||||
"grad_norm": 1.653656005859375,
|
||||
"learning_rate": 6.971499241020495e-06,
|
||||
"loss": 1.6575,
|
||||
"step": 70
|
||||
},
|
||||
{
|
||||
"epoch": 0.7407407407407407,
|
||||
"grad_norm": 1.443210244178772,
|
||||
"learning_rate": 4.095976269940777e-06,
|
||||
"loss": 1.6358,
|
||||
"step": 80
|
||||
},
|
||||
{
|
||||
"epoch": 0.8333333333333334,
|
||||
"grad_norm": 1.2429494857788086,
|
||||
"learning_rate": 1.8343633694278895e-06,
|
||||
"loss": 1.6261,
|
||||
"step": 90
|
||||
},
|
||||
{
|
||||
"epoch": 0.9259259259259259,
|
||||
"grad_norm": 1.2830959558486938,
|
||||
"learning_rate": 4.218267581201296e-07,
|
||||
"loss": 1.6122,
|
||||
"step": 100
|
||||
},
|
||||
{
|
||||
"epoch": 0.9259259259259259,
|
||||
"eval_loss": 1.625947117805481,
|
||||
"eval_runtime": 2.4059,
|
||||
"eval_samples_per_second": 324.208,
|
||||
"eval_steps_per_second": 2.91,
|
||||
"step": 100
|
||||
},
|
||||
{
|
||||
"epoch": 1.0,
|
||||
"step": 108,
|
||||
"total_flos": 4.017624413372416e+16,
|
||||
"train_loss": 1.907559284457454,
|
||||
"train_runtime": 456.0106,
|
||||
"train_samples_per_second": 30.304,
|
||||
"train_steps_per_second": 0.237
|
||||
}
|
||||
],
|
||||
"logging_steps": 10,
|
||||
"max_steps": 108,
|
||||
"num_input_tokens_seen": 0,
|
||||
"num_train_epochs": 1,
|
||||
"save_steps": 200,
|
||||
"stateful_callbacks": {
|
||||
"TrainerControl": {
|
||||
"args": {
|
||||
"should_epoch_stop": false,
|
||||
"should_evaluate": false,
|
||||
"should_log": false,
|
||||
"should_save": true,
|
||||
"should_training_stop": true
|
||||
},
|
||||
"attributes": {}
|
||||
}
|
||||
},
|
||||
"total_flos": 4.017624413372416e+16,
|
||||
"train_batch_size": 16,
|
||||
"trial_name": null,
|
||||
"trial_params": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user