YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
localy trained using my GRPO PR, dataset GSM8k:
Train log:
Starting GRPO training with 5 reward functions..., iters: 500
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> Roses: 25 (25% of 100). Tulips: 40 (80% of 100). Daisies: 35 (70% of 50). None are: 5 (5% of 100). Percentage of flowers that are not roses: (80/100) * 100 = 0.8*100 = 80% </think><answer>0.8</answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
0.8
===================================
Iter 1: Val loss 0.000, Val total_rewards_mean 0.688, Val total_rewards_std 0.400, Val grouped_rewards_mean 0.688, Val grouped_rewards_std 0.400, Val kl 0.001, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.125, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.188, Val r1_count_xml_std 0.188, Val took 31.516s
Iter 10: Train loss 0.000, Total rewards mean 1.086, Total rewards std 0.723, Grouped rewards mean 1.086, Grouped rewards std 0.723, KL 0.001, r1_accuracy_reward_func mean 0.200, r1_accuracy_reward_func std 0.346, r1_int_reward_func mean 0.100, r1_int_reward_func std 0.137, r1_strict_format_reward_func mean 0.112, r1_strict_format_reward_func std 0.122, r1_soft_format_reward_func mean 0.425, r1_soft_format_reward_func std 0.112, r1_count_xml mean 0.249, r1_count_xml std 0.136, Learning Rate 1.000e-05, It/sec 0.028, Tokens/sec 18.284, Peak mem 15.596 GB
Iter 20: Train loss -0.002, Total rewards mean 2.083, Total rewards std 1.205, Grouped rewards mean 2.083, Grouped rewards std 1.205, KL 0.003, r1_accuracy_reward_func mean 0.300, r1_accuracy_reward_func std 0.520, r1_int_reward_func mean 0.175, r1_int_reward_func std 0.230, r1_strict_format_reward_func mean 0.225, r1_strict_format_reward_func std 0.273, r1_soft_format_reward_func mean 0.850, r1_soft_format_reward_func std 0.205, r1_count_xml mean 0.533, r1_count_xml std 0.228, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.647, Peak mem 15.596 GB
Iter 20: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000020_adapters.safetensors.
Iter 30: Train loss -0.001, Total rewards mean 3.324, Total rewards std 1.703, Grouped rewards mean 3.324, Grouped rewards std 1.703, KL 0.007, r1_accuracy_reward_func mean 0.500, r1_accuracy_reward_func std 0.720, r1_int_reward_func mean 0.287, r1_int_reward_func std 0.302, r1_strict_format_reward_func mean 0.438, r1_strict_format_reward_func std 0.481, r1_soft_format_reward_func mean 1.288, r1_soft_format_reward_func std 0.295, r1_count_xml mean 0.812, r1_count_xml std 0.343, Learning Rate 1.000e-05, It/sec 0.037, Tokens/sec 18.429, Peak mem 15.596 GB
Iter 40: Train loss -0.004, Total rewards mean 4.346, Total rewards std 2.036, Grouped rewards mean 4.346, Grouped rewards std 2.036, KL 0.013, r1_accuracy_reward_func mean 0.550, r1_accuracy_reward_func std 0.806, r1_int_reward_func mean 0.350, r1_int_reward_func std 0.392, r1_strict_format_reward_func mean 0.637, r1_strict_format_reward_func std 0.661, r1_soft_format_reward_func mean 1.737, r1_soft_format_reward_func std 0.381, r1_count_xml mean 1.071, r1_count_xml std 0.497, Learning Rate 1.000e-05, It/sec 0.035, Tokens/sec 18.113, Peak mem 15.596 GB
Iter 40: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000040_adapters.safetensors.
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> These are the flowers that are roses: 25 roses. These are the roses that are not roses: 25-4=21. These are the flowers total: 25+40+35=95. The percentage are not roses: 21/95*100=22% </think><answer> 22% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
22%
===================================
Iter 50: Val loss -0.016, Val total_rewards_mean 1.156, Val total_rewards_std 0.379, Val grouped_rewards_mean 1.156, Val grouped_rewards_std 0.379, Val kl 0.012, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 21.807s
Iter 50: Train loss -0.013, Total rewards mean 5.096, Total rewards std 2.520, Grouped rewards mean 5.096, Grouped rewards std 2.520, KL 0.020, r1_accuracy_reward_func mean 0.600, r1_accuracy_reward_func std 0.893, r1_int_reward_func mean 0.387, r1_int_reward_func std 0.456, r1_strict_format_reward_func mean 0.775, r1_strict_format_reward_func std 0.795, r1_soft_format_reward_func mean 2.075, r1_soft_format_reward_func std 0.540, r1_count_xml mean 1.258, r1_count_xml std 0.646, Learning Rate 1.000e-05, It/sec 0.190, Tokens/sec 124.523, Peak mem 15.609 GB
Iter 60: Train loss -0.036, Total rewards mean 6.138, Total rewards std 2.815, Grouped rewards mean 6.138, Grouped rewards std 2.815, KL 0.040, r1_accuracy_reward_func mean 0.600, r1_accuracy_reward_func std 0.893, r1_int_reward_func mean 0.475, r1_int_reward_func std 0.571, r1_strict_format_reward_func mean 0.938, r1_strict_format_reward_func std 0.935, r1_soft_format_reward_func mean 2.550, r1_soft_format_reward_func std 0.583, r1_count_xml mean 1.575, r1_count_xml std 0.740, Learning Rate 1.000e-05, It/sec 0.039, Tokens/sec 17.662, Peak mem 15.609 GB
Iter 60: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000060_adapters.safetensors.
Iter 70: Train loss -0.028, Total rewards mean 7.343, Total rewards std 3.399, Grouped rewards mean 7.343, Grouped rewards std 3.399, KL 0.072, r1_accuracy_reward_func mean 0.750, r1_accuracy_reward_func std 1.079, r1_int_reward_func mean 0.587, r1_int_reward_func std 0.705, r1_strict_format_reward_func mean 1.175, r1_strict_format_reward_func std 1.181, r1_soft_format_reward_func mean 2.987, r1_soft_format_reward_func std 0.673, r1_count_xml mean 1.843, r1_count_xml std 0.863, Learning Rate 1.000e-05, It/sec 0.026, Tokens/sec 17.761, Peak mem 15.609 GB
Iter 80: Train loss -1.025, Total rewards mean 8.592, Total rewards std 3.904, Grouped rewards mean 8.592, Grouped rewards std 3.904, KL 0.134, r1_accuracy_reward_func mean 0.850, r1_accuracy_reward_func std 1.253, r1_int_reward_func mean 0.738, r1_int_reward_func std 0.823, r1_strict_format_reward_func mean 1.438, r1_strict_format_reward_func std 1.339, r1_soft_format_reward_func mean 3.438, r1_soft_format_reward_func std 0.741, r1_count_xml mean 2.130, r1_count_xml std 0.987, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 17.489, Peak mem 15.609 GB
Iter 80: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000080_adapters.safetensors.
Iter 90: Train loss -0.747, Total rewards mean 9.777, Total rewards std 4.470, Grouped rewards mean 9.777, Grouped rewards std 4.470, KL 0.332, r1_accuracy_reward_func mean 0.950, r1_accuracy_reward_func std 1.426, r1_int_reward_func mean 0.837, r1_int_reward_func std 0.960, r1_strict_format_reward_func mean 1.675, r1_strict_format_reward_func std 1.554, r1_soft_format_reward_func mean 3.912, r1_soft_format_reward_func std 0.785, r1_count_xml mean 2.402, r1_count_xml std 1.089, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.673, Peak mem 15.609 GB
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> We first need to add up the total number of flowers in the garden: 25 roses + 40 tulips + 35 daisies = 100 flowers </think> <answer> 100/25 * 100 = 40% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
100/25 * 100 = 40%
===================================
Iter 100: Val loss -1.247, Val total_rewards_mean 0.688, Val total_rewards_std 0.325, Val grouped_rewards_mean 0.688, Val grouped_rewards_std 0.325, Val kl 0.099, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.000, Val r1_strict_format_reward_func_std 0.000, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.312, Val r1_count_xml_std 0.108, Val took 18.438s
Iter 100: Train loss -0.979, Total rewards mean 11.020, Total rewards std 5.108, Grouped rewards mean 11.020, Grouped rewards std 5.108, KL 0.401, r1_accuracy_reward_func mean 1.200, r1_accuracy_reward_func std 1.712, r1_int_reward_func mean 0.925, r1_int_reward_func std 1.050, r1_strict_format_reward_func mean 1.888, r1_strict_format_reward_func std 1.719, r1_soft_format_reward_func mean 4.325, r1_soft_format_reward_func std 0.918, r1_count_xml mean 2.682, r1_count_xml std 1.197, Learning Rate 1.000e-05, It/sec 0.558, Tokens/sec 301.772, Peak mem 15.609 GB
Iter 100: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000100_adapters.safetensors.
Iter 110: Train loss -0.781, Total rewards mean 12.089, Total rewards std 5.403, Grouped rewards mean 12.089, Grouped rewards std 5.403, KL 0.604, r1_accuracy_reward_func mean 1.200, r1_accuracy_reward_func std 1.712, r1_int_reward_func mean 0.975, r1_int_reward_func std 1.118, r1_strict_format_reward_func mean 2.162, r1_strict_format_reward_func std 1.956, r1_soft_format_reward_func mean 4.800, r1_soft_format_reward_func std 0.961, r1_count_xml mean 2.951, r1_count_xml std 1.311, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.646, Peak mem 15.609 GB
Iter 120: Train loss 0.675, Total rewards mean 13.212, Total rewards std 5.702, Grouped rewards mean 13.212, Grouped rewards std 5.702, KL 3.091, r1_accuracy_reward_func mean 1.250, r1_accuracy_reward_func std 1.799, r1_int_reward_func mean 1.013, r1_int_reward_func std 1.164, r1_strict_format_reward_func mean 2.463, r1_strict_format_reward_func std 2.149, r1_soft_format_reward_func mean 5.275, r1_soft_format_reward_func std 1.004, r1_count_xml mean 3.212, r1_count_xml std 1.431, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.644, Peak mem 15.609 GB
Iter 120: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000120_adapters.safetensors.
Iter 130: Train loss -0.338, Total rewards mean 14.361, Total rewards std 6.298, Grouped rewards mean 14.361, Grouped rewards std 6.298, KL 3.149, r1_accuracy_reward_func mean 1.450, r1_accuracy_reward_func std 2.072, r1_int_reward_func mean 1.125, r1_int_reward_func std 1.298, r1_strict_format_reward_func mean 2.650, r1_strict_format_reward_func std 2.351, r1_soft_format_reward_func mean 5.688, r1_soft_format_reward_func std 1.094, r1_count_xml mean 3.448, r1_count_xml std 1.524, Learning Rate 1.000e-05, It/sec 0.032, Tokens/sec 18.707, Peak mem 15.609 GB
Iter 140: Train loss 0.406, Total rewards mean 15.624, Total rewards std 6.804, Grouped rewards mean 15.624, Grouped rewards std 6.804, KL 3.218, r1_accuracy_reward_func mean 1.600, r1_accuracy_reward_func std 2.259, r1_int_reward_func mean 1.250, r1_int_reward_func std 1.434, r1_strict_format_reward_func mean 2.888, r1_strict_format_reward_func std 2.552, r1_soft_format_reward_func mean 6.137, r1_soft_format_reward_func std 1.144, r1_count_xml mean 3.749, r1_count_xml std 1.622, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 18.650, Peak mem 15.609 GB
Iter 140: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000140_adapters.safetensors.
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> We need to find the total number of flowers, subtract the number of roses from that total, and then calculate the percentage that corresponds to the remaining flowers. </think><answer> 84.29% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
84.29%
===================================
Iter 150: Val loss -1.878, Val total_rewards_mean 0.875, Val total_rewards_std 0.508, Val grouped_rewards_mean 0.875, Val grouped_rewards_std 0.508, Val kl 0.091, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.250, Val r1_strict_format_reward_func_std 0.250, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.250, Val r1_count_xml_std 0.153, Val took 15.434s
Iter 150: Train loss 0.307, Total rewards mean 16.830, Total rewards std 7.143, Grouped rewards mean 16.830, Grouped rewards std 7.143, KL 3.288, r1_accuracy_reward_func mean 1.650, r1_accuracy_reward_func std 2.345, r1_int_reward_func mean 1.350, r1_int_reward_func std 1.546, r1_strict_format_reward_func mean 3.150, r1_strict_format_reward_func std 2.749, r1_soft_format_reward_func mean 6.613, r1_soft_format_reward_func std 1.169, r1_count_xml mean 4.068, r1_count_xml std 1.687, Learning Rate 1.000e-05, It/sec 0.449, Tokens/sec 219.298, Peak mem 15.609 GB
Iter 160: Train loss -0.674, Total rewards mean 18.046, Total rewards std 7.711, Grouped rewards mean 18.046, Grouped rewards std 7.711, KL 3.355, r1_accuracy_reward_func mean 1.750, r1_accuracy_reward_func std 2.519, r1_int_reward_func mean 1.500, r1_int_reward_func std 1.701, r1_strict_format_reward_func mean 3.400, r1_strict_format_reward_func std 2.961, r1_soft_format_reward_func mean 7.037, r1_soft_format_reward_func std 1.281, r1_count_xml mean 4.358, r1_count_xml std 1.771, Learning Rate 1.000e-05, It/sec 0.035, Tokens/sec 18.453, Peak mem 15.609 GB
Iter 160: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000160_adapters.safetensors.
Iter 170: Train loss -0.110, Total rewards mean 19.289, Total rewards std 8.190, Grouped rewards mean 19.289, Grouped rewards std 8.190, KL 3.431, r1_accuracy_reward_func mean 1.900, r1_accuracy_reward_func std 2.705, r1_int_reward_func mean 1.575, r1_int_reward_func std 1.787, r1_strict_format_reward_func mean 3.688, r1_strict_format_reward_func std 3.187, r1_soft_format_reward_func mean 7.500, r1_soft_format_reward_func std 1.328, r1_count_xml mean 4.627, r1_count_xml std 1.889, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 17.768, Peak mem 15.609 GB
Iter 180: Train loss -0.537, Total rewards mean 20.361, Total rewards std 8.616, Grouped rewards mean 20.361, Grouped rewards std 8.616, KL 3.495, r1_accuracy_reward_func mean 2.000, r1_accuracy_reward_func std 2.805, r1_int_reward_func mean 1.638, r1_int_reward_func std 1.859, r1_strict_format_reward_func mean 3.900, r1_strict_format_reward_func std 3.414, r1_soft_format_reward_func mean 7.950, r1_soft_format_reward_func std 1.414, r1_count_xml mean 4.873, r1_count_xml std 2.035, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 17.918, Peak mem 15.609 GB
Iter 180: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000180_adapters.safetensors.
Iter 190: Train loss -0.165, Total rewards mean 21.736, Total rewards std 9.399, Grouped rewards mean 21.736, Grouped rewards std 9.399, KL 3.570, r1_accuracy_reward_func mean 2.300, r1_accuracy_reward_func std 3.325, r1_int_reward_func mean 1.775, r1_int_reward_func std 2.061, r1_strict_format_reward_func mean 4.137, r1_strict_format_reward_func std 3.604, r1_soft_format_reward_func mean 8.413, r1_soft_format_reward_func std 1.479, r1_count_xml mean 5.111, r1_count_xml std 2.193, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.594, Peak mem 15.609 GB
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<solution>
In total, 25 + 40 + 35 = 100 flowers.
There are 100 - 25 = 75 flowers that are not roses.
<solution>
<think> The total number of flowers is 25 + 40 + 35 = 100. We need to find the percentage of flowers that are not roses, which is 100 - 25 = 75. </think><answer> 75 </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
75
===================================
Iter 200: Val loss 0.746, Val total_rewards_mean 1.688, Val total_rewards_std 1.267, Val grouped_rewards_mean 1.688, Val grouped_rewards_std 1.267, Val kl 0.045, Val r1_accuracy_reward_func_mean 0.500, Val r1_accuracy_reward_func_std 0.866, Val r1_int_reward_func_mean 0.125, Val r1_int_reward_func_std 0.217, Val r1_strict_format_reward_func_mean 0.250, Val r1_strict_format_reward_func_std 0.250, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.312, Val r1_count_xml_std 0.108, Val took 26.068s
Iter 200: Train loss -0.091, Total rewards mean 22.944, Total rewards std 9.915, Grouped rewards mean 22.944, Grouped rewards std 9.915, KL 3.619, r1_accuracy_reward_func mean 2.450, r1_accuracy_reward_func std 3.585, r1_int_reward_func mean 1.850, r1_int_reward_func std 2.172, r1_strict_format_reward_func mean 4.438, r1_strict_format_reward_func std 3.834, r1_soft_format_reward_func mean 8.875, r1_soft_format_reward_func std 1.544, r1_count_xml mean 5.332, r1_count_xml std 2.360, Learning Rate 1.000e-05, It/sec 0.297, Tokens/sec 188.080, Peak mem 15.609 GB
Iter 200: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000200_adapters.safetensors.
Iter 210: Train loss -0.321, Total rewards mean 23.972, Total rewards std 10.221, Grouped rewards mean 23.972, Grouped rewards std 10.221, KL 3.687, r1_accuracy_reward_func mean 2.450, r1_accuracy_reward_func std 3.585, r1_int_reward_func mean 1.888, r1_int_reward_func std 2.219, r1_strict_format_reward_func mean 4.688, r1_strict_format_reward_func std 4.032, r1_soft_format_reward_func mean 9.350, r1_soft_format_reward_func std 1.587, r1_count_xml mean 5.597, r1_count_xml std 2.473, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 18.288, Peak mem 15.609 GB
Iter 220: Train loss -0.912, Total rewards mean 25.083, Total rewards std 10.699, Grouped rewards mean 25.083, Grouped rewards std 10.699, KL 3.755, r1_accuracy_reward_func mean 2.550, r1_accuracy_reward_func std 3.758, r1_int_reward_func mean 1.975, r1_int_reward_func std 2.309, r1_strict_format_reward_func mean 4.938, r1_strict_format_reward_func std 4.255, r1_soft_format_reward_func mean 9.800, r1_soft_format_reward_func std 1.656, r1_count_xml mean 5.820, r1_count_xml std 2.616, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.307, Peak mem 15.609 GB
Iter 220: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000220_adapters.safetensors.
Iter 230: Train loss -1.596, Total rewards mean 26.009, Total rewards std 11.108, Grouped rewards mean 26.009, Grouped rewards std 11.108, KL 3.824, r1_accuracy_reward_func mean 2.550, r1_accuracy_reward_func std 3.758, r1_int_reward_func mean 2.037, r1_int_reward_func std 2.374, r1_strict_format_reward_func mean 5.137, r1_strict_format_reward_func std 4.467, r1_soft_format_reward_func mean 10.200, r1_soft_format_reward_func std 1.811, r1_count_xml mean 6.084, r1_count_xml std 2.756, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.324, Peak mem 15.609 GB
Iter 240: Train loss 0.491, Total rewards mean 27.309, Total rewards std 11.639, Grouped rewards mean 27.309, Grouped rewards std 11.639, KL 3.904, r1_accuracy_reward_func mean 2.800, r1_accuracy_reward_func std 4.118, r1_int_reward_func mean 2.150, r1_int_reward_func std 2.464, r1_strict_format_reward_func mean 5.338, r1_strict_format_reward_func std 4.665, r1_soft_format_reward_func mean 10.688, r1_soft_format_reward_func std 1.832, r1_count_xml mean 6.334, r1_count_xml std 2.870, Learning Rate 1.000e-05, It/sec 0.032, Tokens/sec 18.672, Peak mem 15.609 GB
Iter 240: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000240_adapters.safetensors.
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> The total number of flowers in the garden is 25 + 40 + 35 = 100. The number of flowers that are not roses is 40 + 35 = 75. The percentage of flowers that are not roses is 75/100 * 100 = 75% </think><answer> 75% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
75%
===================================
Iter 250: Val loss -2.211, Val total_rewards_mean 1.250, Val total_rewards_std 0.217, Val grouped_rewards_mean 1.250, Val grouped_rewards_std 0.217, Val kl 0.097, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.375, Val r1_count_xml_std 0.000, Val took 15.147s
Iter 250: Train loss -1.791, Total rewards mean 28.546, Total rewards std 12.248, Grouped rewards mean 28.546, Grouped rewards std 12.248, KL 3.968, r1_accuracy_reward_func mean 2.950, r1_accuracy_reward_func std 4.377, r1_int_reward_func mean 2.237, r1_int_reward_func std 2.597, r1_strict_format_reward_func mean 5.588, r1_strict_format_reward_func std 4.895, r1_soft_format_reward_func mean 11.163, r1_soft_format_reward_func std 1.876, r1_count_xml mean 6.608, r1_count_xml std 2.981, Learning Rate 1.000e-05, It/sec 0.293, Tokens/sec 156.096, Peak mem 15.609 GB
Iter 260: Train loss -0.323, Total rewards mean 30.033, Total rewards std 13.043, Grouped rewards mean 30.033, Grouped rewards std 13.043, KL 4.021, r1_accuracy_reward_func mean 3.300, r1_accuracy_reward_func std 4.984, r1_int_reward_func mean 2.463, r1_int_reward_func std 2.752, r1_strict_format_reward_func mean 5.800, r1_strict_format_reward_func std 5.115, r1_soft_format_reward_func mean 11.613, r1_soft_format_reward_func std 1.944, r1_count_xml mean 6.858, r1_count_xml std 3.125, Learning Rate 1.000e-05, It/sec 0.027, Tokens/sec 18.002, Peak mem 19.129 GB
Iter 260: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000260_adapters.safetensors.
Iter 270: Train loss 0.025, Total rewards mean 31.266, Total rewards std 13.536, Grouped rewards mean 31.266, Grouped rewards std 13.536, KL 4.082, r1_accuracy_reward_func mean 3.400, r1_accuracy_reward_func std 5.157, r1_int_reward_func mean 2.537, r1_int_reward_func std 2.845, r1_strict_format_reward_func mean 6.137, r1_strict_format_reward_func std 5.273, r1_soft_format_reward_func mean 12.075, r1_soft_format_reward_func std 2.009, r1_count_xml mean 7.116, r1_count_xml std 3.244, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.453, Peak mem 19.129 GB
Iter 280: Train loss -2.500, Total rewards mean 32.471, Total rewards std 13.946, Grouped rewards mean 32.471, Grouped rewards std 13.946, KL 4.177, r1_accuracy_reward_func mean 3.500, r1_accuracy_reward_func std 5.330, r1_int_reward_func mean 2.600, r1_int_reward_func std 2.935, r1_strict_format_reward_func mean 6.400, r1_strict_format_reward_func std 5.481, r1_soft_format_reward_func mean 12.538, r1_soft_format_reward_func std 2.055, r1_count_xml mean 7.434, r1_count_xml std 3.317, Learning Rate 1.000e-05, It/sec 0.039, Tokens/sec 18.447, Peak mem 19.129 GB
Iter 280: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000280_adapters.safetensors.
Iter 290: Train loss 0.255, Total rewards mean 33.556, Total rewards std 14.219, Grouped rewards mean 33.556, Grouped rewards std 14.219, KL 4.265, r1_accuracy_reward_func mean 3.500, r1_accuracy_reward_func std 5.330, r1_int_reward_func mean 2.625, r1_int_reward_func std 2.978, r1_strict_format_reward_func mean 6.688, r1_strict_format_reward_func std 5.683, r1_soft_format_reward_func mean 13.025, r1_soft_format_reward_func std 2.077, r1_count_xml mean 7.718, r1_count_xml std 3.432, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 18.379, Peak mem 19.129 GB
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> We can think about the percentage here as a fraction. There are a total of 60 flowers. We are interested in the percentage of other flowers. 60 - 25 = 35 <answer> 58.33% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
58.33%
===================================
Iter 300: Val loss -1.223, Val total_rewards_mean 0.688, Val total_rewards_std 0.325, Val grouped_rewards_mean 0.688, Val grouped_rewards_std 0.325, Val kl 0.058, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.250, Val r1_strict_format_reward_func_std 0.250, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.062, Val r1_count_xml_std 0.108, Val took 20.780s
Iter 300: Train loss 0.123, Total rewards mean 34.595, Total rewards std 14.587, Grouped rewards mean 34.595, Grouped rewards std 14.587, KL 4.339, r1_accuracy_reward_func mean 3.550, r1_accuracy_reward_func std 5.417, r1_int_reward_func mean 2.662, r1_int_reward_func std 3.043, r1_strict_format_reward_func mean 6.912, r1_strict_format_reward_func std 5.894, r1_soft_format_reward_func mean 13.500, r1_soft_format_reward_func std 2.120, r1_count_xml mean 7.970, r1_count_xml std 3.569, Learning Rate 1.000e-05, It/sec 0.446, Tokens/sec 240.707, Peak mem 19.129 GB
Iter 300: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000300_adapters.safetensors.
Iter 310: Train loss -1.306, Total rewards mean 35.881, Total rewards std 15.126, Grouped rewards mean 35.881, Grouped rewards std 15.126, KL 4.431, r1_accuracy_reward_func mean 3.700, r1_accuracy_reward_func std 5.677, r1_int_reward_func mean 2.737, r1_int_reward_func std 3.130, r1_strict_format_reward_func mean 7.275, r1_strict_format_reward_func std 6.028, r1_soft_format_reward_func mean 13.938, r1_soft_format_reward_func std 2.229, r1_count_xml mean 8.231, r1_count_xml std 3.686, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.488, Peak mem 19.129 GB
Iter 320: Train loss -2.792, Total rewards mean 36.951, Total rewards std 15.523, Grouped rewards mean 36.951, Grouped rewards std 15.523, KL 4.533, r1_accuracy_reward_func mean 3.750, r1_accuracy_reward_func std 5.763, r1_int_reward_func mean 2.812, r1_int_reward_func std 3.242, r1_strict_format_reward_func mean 7.537, r1_strict_format_reward_func std 6.192, r1_soft_format_reward_func mean 14.363, r1_soft_format_reward_func std 2.340, r1_count_xml mean 8.489, r1_count_xml std 3.838, Learning Rate 1.000e-05, It/sec 0.041, Tokens/sec 18.199, Peak mem 19.129 GB
Iter 320: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000320_adapters.safetensors.
Iter 330: Train loss 0.071, Total rewards mean 38.183, Total rewards std 16.066, Grouped rewards mean 38.183, Grouped rewards std 16.066, KL 4.598, r1_accuracy_reward_func mean 3.900, r1_accuracy_reward_func std 6.023, r1_int_reward_func mean 2.912, r1_int_reward_func std 3.397, r1_strict_format_reward_func mean 7.750, r1_strict_format_reward_func std 6.394, r1_soft_format_reward_func mean 14.850, r1_soft_format_reward_func std 2.362, r1_count_xml mean 8.770, r1_count_xml std 3.948, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.575, Peak mem 19.129 GB
Iter 340: Train loss -0.522, Total rewards mean 39.573, Total rewards std 16.618, Grouped rewards mean 39.573, Grouped rewards std 16.618, KL 4.700, r1_accuracy_reward_func mean 4.150, r1_accuracy_reward_func std 6.310, r1_int_reward_func mean 3.013, r1_int_reward_func std 3.533, r1_strict_format_reward_func mean 8.038, r1_strict_format_reward_func std 6.584, r1_soft_format_reward_func mean 15.337, r1_soft_format_reward_func std 2.384, r1_count_xml mean 9.036, r1_count_xml std 4.027, Learning Rate 1.000e-05, It/sec 0.039, Tokens/sec 18.497, Peak mem 19.129 GB
Iter 340: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000340_adapters.safetensors.
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> To find out what percentage of flowers are not roses, we need to follow these steps: </think>
<answer> Calculate the total number of flowers in the garden. Subtract the number of roses from the total number of flowers to find the number of flowers that are neither roses nor tulips or daisies. Then, find the percentage of flowers that are not roses by dividing the number of flowers that are not roses by the total number of flowers and multiplying by 100. </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
Calculate the total number of flowers in the garden. Subtract the number of roses from the total number of flowers to find the number of flowers that are neither roses nor tulips or daisies. Then, find the percentage of flowers that are not roses by dividing the number of flowers that are not roses by the total number of flowers and multiplying by 100.
===================================
Iter 350: Val loss -2.649, Val total_rewards_mean 1.156, Val total_rewards_std 0.223, Val grouped_rewards_mean 1.156, Val grouped_rewards_std 0.223, Val kl 0.131, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 14.459s
Iter 350: Train loss -0.594, Total rewards mean 40.711, Total rewards std 17.084, Grouped rewards mean 40.711, Grouped rewards std 17.084, KL 4.775, r1_accuracy_reward_func mean 4.250, r1_accuracy_reward_func std 6.483, r1_int_reward_func mean 3.075, r1_int_reward_func std 3.623, r1_strict_format_reward_func mean 8.275, r1_strict_format_reward_func std 6.786, r1_soft_format_reward_func mean 15.800, r1_soft_format_reward_func std 2.449, r1_count_xml mean 9.311, r1_count_xml std 4.137, Learning Rate 1.000e-05, It/sec 0.326, Tokens/sec 187.077, Peak mem 19.129 GB
Iter 360: Train loss -0.318, Total rewards mean 41.833, Total rewards std 17.455, Grouped rewards mean 41.833, Grouped rewards std 17.455, KL 4.857, r1_accuracy_reward_func mean 4.300, r1_accuracy_reward_func std 6.569, r1_int_reward_func mean 3.162, r1_int_reward_func std 3.695, r1_strict_format_reward_func mean 8.525, r1_strict_format_reward_func std 6.959, r1_soft_format_reward_func mean 16.250, r1_soft_format_reward_func std 2.535, r1_count_xml mean 9.596, r1_count_xml std 4.234, Learning Rate 1.000e-05, It/sec 0.037, Tokens/sec 18.497, Peak mem 19.129 GB
Iter 360: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000360_adapters.safetensors.
Iter 370: Train loss 0.467, Total rewards mean 43.073, Total rewards std 17.890, Grouped rewards mean 43.073, Grouped rewards std 17.890, KL 4.972, r1_accuracy_reward_func mean 4.450, r1_accuracy_reward_func std 6.756, r1_int_reward_func mean 3.237, r1_int_reward_func std 3.770, r1_strict_format_reward_func mean 8.812, r1_strict_format_reward_func std 7.149, r1_soft_format_reward_func mean 16.725, r1_soft_format_reward_func std 2.578, r1_count_xml mean 9.848, r1_count_xml std 4.373, Learning Rate 1.000e-05, It/sec 0.040, Tokens/sec 18.342, Peak mem 19.129 GB
Iter 380: Train loss -0.234, Total rewards mean 44.282, Total rewards std 18.515, Grouped rewards mean 44.282, Grouped rewards std 18.515, KL 5.046, r1_accuracy_reward_func mean 4.600, r1_accuracy_reward_func std 7.016, r1_int_reward_func mean 3.287, r1_int_reward_func std 3.856, r1_strict_format_reward_func mean 9.075, r1_strict_format_reward_func std 7.375, r1_soft_format_reward_func mean 17.188, r1_soft_format_reward_func std 2.643, r1_count_xml mean 10.132, r1_count_xml std 4.491, Learning Rate 1.000e-05, It/sec 0.035, Tokens/sec 18.543, Peak mem 19.129 GB
Iter 380: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000380_adapters.safetensors.
Iter 390: Train loss -0.686, Total rewards mean 45.690, Total rewards std 19.125, Grouped rewards mean 45.690, Grouped rewards std 19.125, KL 5.122, r1_accuracy_reward_func mean 4.900, r1_accuracy_reward_func std 7.289, r1_int_reward_func mean 3.425, r1_int_reward_func std 3.996, r1_strict_format_reward_func mean 9.375, r1_strict_format_reward_func std 7.580, r1_soft_format_reward_func mean 17.663, r1_soft_format_reward_func std 2.687, r1_count_xml mean 10.328, r1_count_xml std 4.636, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.480, Peak mem 19.129 GB
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> Calculate the total number of flowers </think><answer> 25 + 40 + 35 = 100 </answer> <think> Calculate the number of flowers that are not roses </think><answer> 100 - 25 = 75 </answer> <think> Calculate the percentage of flowers that are not roses </think><answer> 75 / 100 = 75% </answer> 75% of the flowers are not roses.
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
75 / 100 = 75%
===================================
Iter 400: Val loss -3.355, Val total_rewards_mean 1.281, Val total_rewards_std 0.162, Val grouped_rewards_mean 1.281, Val grouped_rewards_std 0.162, Val kl 0.149, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.500, Val r1_strict_format_reward_func_std 0.000, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 17.742s
Iter 400: Train loss -0.257, Total rewards mean 46.887, Total rewards std 19.556, Grouped rewards mean 46.887, Grouped rewards std 19.556, KL 5.216, r1_accuracy_reward_func mean 5.000, r1_accuracy_reward_func std 7.462, r1_int_reward_func mean 3.500, r1_int_reward_func std 4.065, r1_strict_format_reward_func mean 9.675, r1_strict_format_reward_func std 7.742, r1_soft_format_reward_func mean 18.112, r1_soft_format_reward_func std 2.755, r1_count_xml mean 10.599, r1_count_xml std 4.770, Learning Rate 1.000e-05, It/sec 0.559, Tokens/sec 286.081, Peak mem 19.129 GB
Iter 400: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000400_adapters.safetensors.
Iter 410: Train loss -0.331, Total rewards mean 48.110, Total rewards std 20.060, Grouped rewards mean 48.110, Grouped rewards std 20.060, KL 5.354, r1_accuracy_reward_func mean 5.150, r1_accuracy_reward_func std 7.722, r1_int_reward_func mean 3.612, r1_int_reward_func std 4.198, r1_strict_format_reward_func mean 9.950, r1_strict_format_reward_func std 7.953, r1_soft_format_reward_func mean 18.550, r1_soft_format_reward_func std 2.863, r1_count_xml mean 10.848, r1_count_xml std 4.897, Learning Rate 1.000e-05, It/sec 0.030, Tokens/sec 18.754, Peak mem 19.129 GB
Iter 420: Train loss -0.031, Total rewards mean 49.229, Total rewards std 20.523, Grouped rewards mean 49.229, Grouped rewards std 20.523, KL 5.439, r1_accuracy_reward_func mean 5.200, r1_accuracy_reward_func std 7.809, r1_int_reward_func mean 3.688, r1_int_reward_func std 4.266, r1_strict_format_reward_func mean 10.212, r1_strict_format_reward_func std 8.187, r1_soft_format_reward_func mean 18.975, r1_soft_format_reward_func std 2.975, r1_count_xml mean 11.154, r1_count_xml std 4.997, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.377, Peak mem 19.129 GB
Iter 420: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000420_adapters.safetensors.
Iter 430: Train loss 20.228, Total rewards mean 50.436, Total rewards std 21.019, Grouped rewards mean 50.436, Grouped rewards std 21.019, KL 210.900, r1_accuracy_reward_func mean 5.300, r1_accuracy_reward_func std 7.982, r1_int_reward_func mean 3.800, r1_int_reward_func std 4.388, r1_strict_format_reward_func mean 10.462, r1_strict_format_reward_func std 8.391, r1_soft_format_reward_func mean 19.438, r1_soft_format_reward_func std 3.040, r1_count_xml mean 11.436, r1_count_xml std 5.111, Learning Rate 1.000e-05, It/sec 0.041, Tokens/sec 18.370, Peak mem 19.129 GB
Iter 440: Train loss -0.072, Total rewards mean 51.886, Total rewards std 21.842, Grouped rewards mean 51.886, Grouped rewards std 21.842, KL 210.971, r1_accuracy_reward_func mean 5.700, r1_accuracy_reward_func std 8.528, r1_int_reward_func mean 3.900, r1_int_reward_func std 4.524, r1_strict_format_reward_func mean 10.700, r1_strict_format_reward_func std 8.525, r1_soft_format_reward_func mean 19.900, r1_soft_format_reward_func std 3.105, r1_count_xml mean 11.686, r1_count_xml std 5.280, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.497, Peak mem 19.129 GB
Iter 440: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000440_adapters.safetensors.
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> calculating the total number of flowers </think><answer> 25 + 40 + 35 = 100 </answer><think> Calculating the number of non-rose flowers </think><answer> 40 + 35 = 75 </answer><think> Calculating percentage of non-rose flowers </think><answer> (75 / 100) * 100 = 75% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
(75 / 100) * 100 = 75%
===================================
Iter 450: Val loss -0.342, Val total_rewards_mean 1.156, Val total_rewards_std 0.223, Val grouped_rewards_mean 1.156, Val grouped_rewards_std 0.223, Val kl 0.112, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 15.687s
Iter 450: Train loss -0.592, Total rewards mean 52.918, Total rewards std 22.259, Grouped rewards mean 52.918, Grouped rewards std 22.259, KL 211.036, r1_accuracy_reward_func mean 5.750, r1_accuracy_reward_func std 8.615, r1_int_reward_func mean 3.987, r1_int_reward_func std 4.621, r1_strict_format_reward_func mean 10.925, r1_strict_format_reward_func std 8.761, r1_soft_format_reward_func mean 20.350, r1_soft_format_reward_func std 3.173, r1_count_xml mean 11.905, r1_count_xml std 5.424, Learning Rate 1.000e-05, It/sec 0.307, Tokens/sec 202.405, Peak mem 19.129 GB
Iter 460: Train loss -0.369, Total rewards mean 54.165, Total rewards std 22.714, Grouped rewards mean 54.165, Grouped rewards std 22.714, KL 211.112, r1_accuracy_reward_func mean 5.900, r1_accuracy_reward_func std 8.875, r1_int_reward_func mean 4.062, r1_int_reward_func std 4.696, r1_strict_format_reward_func mean 11.225, r1_strict_format_reward_func std 8.941, r1_soft_format_reward_func mean 20.825, r1_soft_format_reward_func std 3.216, r1_count_xml mean 12.152, r1_count_xml std 5.502, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.610, Peak mem 19.129 GB
Iter 460: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000460_adapters.safetensors.
Iter 470: Train loss -0.346, Total rewards mean 55.242, Total rewards std 23.097, Grouped rewards mean 55.242, Grouped rewards std 23.097, KL 211.168, r1_accuracy_reward_func mean 5.900, r1_accuracy_reward_func std 8.875, r1_int_reward_func mean 4.137, r1_int_reward_func std 4.808, r1_strict_format_reward_func mean 11.512, r1_strict_format_reward_func std 9.125, r1_soft_format_reward_func mean 21.275, r1_soft_format_reward_func std 3.303, r1_count_xml mean 12.417, r1_count_xml std 5.624, Learning Rate 1.000e-05, It/sec 0.027, Tokens/sec 18.566, Peak mem 19.129 GB
Iter 480: Train loss -0.784, Total rewards mean 56.456, Total rewards std 23.559, Grouped rewards mean 56.456, Grouped rewards std 23.559, KL 211.253, r1_accuracy_reward_func mean 6.000, r1_accuracy_reward_func std 9.048, r1_int_reward_func mean 4.238, r1_int_reward_func std 4.908, r1_strict_format_reward_func mean 11.738, r1_strict_format_reward_func std 9.329, r1_soft_format_reward_func mean 21.763, r1_soft_format_reward_func std 3.325, r1_count_xml mean 12.718, r1_count_xml std 5.714, Learning Rate 1.000e-05, It/sec 0.041, Tokens/sec 18.347, Peak mem 19.129 GB
Iter 480: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000480_adapters.safetensors.
Iter 490: Train loss -0.775, Total rewards mean 57.515, Total rewards std 23.916, Grouped rewards mean 57.515, Grouped rewards std 23.916, KL 211.321, r1_accuracy_reward_func mean 6.050, r1_accuracy_reward_func std 9.134, r1_int_reward_func mean 4.300, r1_int_reward_func std 4.998, r1_strict_format_reward_func mean 12.012, r1_strict_format_reward_func std 9.491, r1_soft_format_reward_func mean 22.225, r1_soft_format_reward_func std 3.390, r1_count_xml mean 12.928, r1_count_xml std 5.847, Learning Rate 1.000e-05, It/sec 0.029, Tokens/sec 18.415, Peak mem 19.129 GB
=== Validation Sample Details ===
📋 Raw Prompt:
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> We can calculate the total number of plants in the garden by adding the number of each type of plant together: </think><answer>total plants=25+40+35=100 </answer><think> Number of non-rose plants is 100-25=75 </think><answer>Number of non-rose plants is 100-25=75 </answer><think> The percentage of not-rose flowers is 75/100*100=75% </think><answer>75% </answer>
==========
✅ Answer:
75
==========
🔍 Extracted Answer:
75%
===================================
Iter 500: Val loss -0.600, Val total_rewards_mean 1.094, Val total_rewards_std 0.495, Val grouped_rewards_mean 1.094, Val grouped_rewards_std 0.495, Val kl 0.091, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.125, Val r1_int_reward_func_std 0.217, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.094, Val r1_count_xml_std 0.162, Val took 17.482s
Iter 500: Train loss -1.223, Total rewards mean 58.690, Total rewards std 24.391, Grouped rewards mean 58.690, Grouped rewards std 24.391, KL 211.407, r1_accuracy_reward_func mean 6.150, r1_accuracy_reward_func std 9.308, r1_int_reward_func mean 4.387, r1_int_reward_func std 5.113, r1_strict_format_reward_func mean 12.238, r1_strict_format_reward_func std 9.728, r1_soft_format_reward_func mean 22.688, r1_soft_format_reward_func std 3.436, r1_count_xml mean 13.228, r1_count_xml std 5.943, Learning Rate 1.000e-05, It/sec 0.499, Tokens/sec 257.881, Peak mem 19.129 GB
Iter 500: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000500_adapters.safetensors.
Saved final weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors.
Testing
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=== Validation Sample Details ===
📋 Raw Prompt:
Arnel had ten boxes of pencils with the same number of pencils in each box. He kept ten pencils and shared the remaining pencils equally with his five friends. If his friends got eight pencils each, how many pencils are in each box?.
==========
🔄 Model Input:
<|im_start|>system
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
<|im_start|>user
Arnel had ten boxes of pencils with the same number of pencils in each box. He kept ten pencils and shared the remaining pencils equally with his five friends. If his friends got eight pencils each, how many pencils are in each box?.<|im_end|>
<|im_start|>assistant
==========
📝 Generation:
<think> Let's denote the number of pencils in each box as \( x \). Arnel has \( 10x \) pencils in total, and he kept 10 pencils for himself, leaving him with \( 10x - 10 \) pencils to share. Since he has 5 friends, and each friend gets 8 pencils, the total number of pencils shared among his friends is \( 5 \times 8 = 40 \). Therefore, we can write the equation: \[ 10x - 10 = 40 \] \[ 10x = 40 + 10 \] \[ 10x = 50 \] \[ x = 50 / 10 \] \[ x = 5 \] </think><answer>5</answer>
==========
✅ Answer:
5
==========
🔍 Extracted Answer:
5
===================================
Test loss -0.147, Test ppl 0.863, Rewards: total_rewards_mean: 2.438, total_rewards_std: 1.204, grouped_rewards_mean: 2.438, grouped_rewards_std: 1.204, kl: 0.051, r1_accuracy_reward_func_mean: 1.000, r1_accuracy_reward_func_std: 1.000, r1_int_reward_func_mean: 0.375, r1_int_reward_func_std: 0.217, r1_strict_format_reward_func_mean: 0.250, r1_strict_format_reward_func_std: 0.250, r1_soft_format_reward_func_mean: 0.500, r1_soft_format_reward_func_std: 0.000, r1_count_xml_mean: 0.312, r1_count_xml_std: 0.108
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