Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758
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joke-finetome-model-gguf-ph…/reports/report.html
ModelHub XC 2c2240df42 初始化项目，由ModelHub XC社区提供模型 
Model: Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758
Source: Original Platform
2026-04-11 12:30:59 +08:00

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<!doctype html>
<html>
<head>
<meta charset="utf-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>Training Report</title>
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pre { margin: 8px 0 0; white-space: pre-wrap; word-wrap: break-word; }
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</style>
</head>
<body>
<header>
<div style="font-size:18px;font-weight:700;">Training Report</div>
<div class="muted">Single-file HTML (dashboard + run payload)</div>
</header>
<div class="wrap">
<details open><summary>Notes</summary><pre></pre></details>
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<details><summary>Run meta (quick)</summary><pre>{
&quot;model&quot;: &quot;unsloth/Phi-4-unsloth-bnb-4bit&quot;,
&quot;dataset&quot;: &quot;Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl&quot;,
&quot;examples_total&quot;: 2872,
&quot;examples_train&quot;: 1436,
&quot;examples_eval&quot;: 1436,
&quot;world_size&quot;: 1,
&quot;effective_batch_size&quot;: 8,
&quot;steps_per_epoch_approx&quot;: 179.5,
&quot;max_steps&quot;: 2000,
&quot;eval_steps&quot;: 50,
&quot;save_steps&quot;: 50,
&quot;learning_rate&quot;: 9.95267419777795e-06,
&quot;warmup_steps&quot;: 10,
&quot;lr_scheduler_type&quot;: &quot;linear&quot;,
&quot;weight_decay&quot;: 0.009206070410847844,
&quot;lora_r&quot;: 32,
&quot;lora_alpha&quot;: 64,
&quot;lora_dropout&quot;: 0.0,
&quot;best_checkpoint&quot;: &quot;outputs/continue_r1_from_350_20260112_073729/checkpoint-100&quot;,
&quot;LR_AUTO_ENABLED&quot;: true,
&quot;LR_AUTO_USE_N&quot;: &quot;train&quot;,
&quot;LR_AUTO_N_REF&quot;: 1436,
&quot;LR_AUTO_BASE&quot;: 1e-05,
&quot;LR_AUTO_MULT&quot;: 0.5,
&quot;LR_AUTO_FINAL&quot;: 5e-06,
&quot;best_step&quot;: 100,
&quot;best_eval_loss&quot;: 2.2380564212799072,
&quot;best_blended&quot;: 1.3520409573791146,
&quot;best_blended_step&quot;: 600
}</pre></details>
<details><summary>config_snapshot</summary><pre>{
&quot;MODEL_NAME&quot;: &quot;unsloth/Phi-4-unsloth-bnb-4bit&quot;,
&quot;CHAT_TEMPLATE&quot;: &quot;phi-4&quot;,
&quot;MAX_SEQ_LENGTH&quot;: 2048,
&quot;LOAD_IN_4BIT&quot;: true,
&quot;DATASET_NAME&quot;: &quot;Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl&quot;,
&quot;DATASET_SPLIT&quot;: &quot;train&quot;,
&quot;PER_DEVICE_TRAIN_BATCH_SIZE&quot;: 2,
&quot;GRADIENT_ACCUMULATION_STEPS&quot;: 4,
&quot;WARMUP_STEPS&quot;: 10,
&quot;MAX_STEPS&quot;: 2000,
&quot;LEARNING_RATE&quot;: 9.95267419777795e-06,
&quot;WEIGHT_DECAY&quot;: 0.009206070410847844,
&quot;LR_SCHEDULER_TYPE&quot;: &quot;linear&quot;,
&quot;SEED&quot;: 3407,
&quot;PLOTLY_DARK_MODE&quot;: true,
&quot;PLOTLY_BASE_COLOR&quot;: &quot;#00CC96&quot;,
&quot;PLOTLY_EMA_SPAN&quot;: 25,
&quot;LR_AUTO_ENABLED&quot;: true,
&quot;LR_AUTO_USE_N&quot;: &quot;train&quot;,
&quot;LR_AUTO_N_REF&quot;: 1436,
&quot;LR_AUTO_BASE&quot;: 1e-05,
&quot;LR_AUTO_MULT&quot;: 0.5,
&quot;LR_AUTO_FINAL&quot;: 5e-06
}</pre></details>
<details><summary>run_manifest</summary><pre>{
&quot;model_name&quot;: &quot;unsloth/Phi-4-unsloth-bnb-4bit&quot;,
&quot;dataset&quot;: {
&quot;name&quot;: &quot;Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl&quot;,
&quot;split&quot;: &quot;train&quot;
},
&quot;training&quot;: {
&quot;max_steps&quot;: 2000,
&quot;learning_rate&quot;: 9.95267419777795e-06,
&quot;per_device_train_batch_size&quot;: 2,
&quot;gradient_accumulation_steps&quot;: 4,
&quot;max_seq_length&quot;: 2048,
&quot;seed&quot;: 3407,
&quot;optimizer&quot;: &quot;adamw_8bit&quot;,
&quot;lr_scheduler_type&quot;: &quot;linear&quot;
},
&quot;auto_lr&quot;: {
&quot;enabled&quot;: true,
&quot;use_n&quot;: &quot;train&quot;,
&quot;n_ref&quot;: 1436,
&quot;base&quot;: 1e-05,
&quot;mult&quot;: 0.5,
&quot;final&quot;: 5e-06
},
&quot;best&quot;: {
&quot;checkpoint&quot;: &quot;/content/outputs/continue_r1_from_350_20260112_073729/checkpoint-100&quot;,
&quot;metric&quot;: 2.2380564212799072,
&quot;metric_name&quot;: &quot;eval_loss&quot;
},
&quot;plotly&quot;: {
&quot;html&quot;: &quot;training_loss_step.html&quot;
}
}</pre></details>
<details><summary>trainer.state.log_history</summary><pre>[
{
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