File size: 4,400 Bytes
707cacf 7a5980d 1a0631e 707cacf 1a0631e 7a5980d bc6710d 94abbdc bc6710d 1a0631e 034877d 7a5980d 1a0631e 94abbdc 1a0631e 94abbdc 1a0631e 94abbdc 1a0631e 94abbdc 1a0631e 94abbdc 1a0631e 84ca6e4 1a0631e 94abbdc bd1f758 1a0631e 7a5980d bd1f758 7a5980d 707cacf 1a0631e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
---
library_name: peft
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: sn_math_curator_on_ensemble_8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
This is an open-source fine-tuned reasoning adapter of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct), transformed into a math reasoning model using data curated from [collinear-ai/R1-Distill-SFT-Curated](https://huggingface.co/datasets/collinear-ai/R1-Distill-SFT-Curated).
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl version</summary>
axolotl version: `0.5.0`
<!-- ```yaml
strict: false
base_model: microsoft/Phi-3.5-mini-instruct
tokenizer_config: microsoft/Phi-3.5-mini-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Output configuration
hub_model_id: collinear-ai/sn_math_curator_on_ensemble_8
dataset_prepared_path: data/sn_math_curator_on_ensemble_8
output_dir: model/sn_math_curator_on_ensemble_8
# Format the dataset into the right instruction format.
chat_template: phi_3
datasets:
- path: collinear-ai/R1-Distill-SFT-numina-math-ensemble_8_train
split: train
type: chat_template
chat_template: phi_3
field_messages: train_conv
message_field_role: role
message_field_content: content
train_on_inputs: false #FALSE
val_set_size: 0.05
# Data packing
sequence_len: 4096
eval_sample_packing: false
sample_packing: false
pad_to_sequence_len: true
group_by_length: false
# Lora config
adapter: qlora
lora_model_dir:
load_in_8bit: false
load_in_4bit: true -->
<!-- lora_r: 128
lora_alpha: 64
lora_dropout: 0.2
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
lora_modules_to_save:
- embed_tokens
- lm_head
# Logging config
wandb_project: sn-curators-downstream
wandb_entity: nazneen
wandb_name: curator_math_sn_ensemble_8_phi
# Trainer config
gradient_accumulation_steps: 2
micro_batch_size: 10
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.000005
bfloat16: true
bf16: true
fp16:
tf32: false -->
<!-- gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 10
xformers_attention:
flash_attention: true
save_safetensors: true
warmup_steps: 50
evals_per_epoch: 3
eval_table_size: 3
eval_max_new_tokens: 2048
saves_per_epoch: 40
debug:
deepspeed:
weight_decay: 0.02
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|endoftext|>"
unk_token: "<unk>"
pad_token: "<|endoftext|>"
``` -->
</details><br>
## Intended uses & limitations
Math-Reasoning
## Training and evaluation data
Training data curated from [collinear-ai/R1-Distill-SFT-Curated](https://huggingface.co/datasets/collinear-ai/R1-Distill-SFT-Curated)
Evaluation data: [HuggingFaceH4/MATH-500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 160
- total_eval_batch_size: 80
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | 0.6646 |
| 0.3174 | 0.3335 | 1247 | 0.3329 |
| 0.307 | 0.6670 | 2494 | 0.3169 |
### Evaluation on Math500
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3 |