See axolotl config
axolotl version: 0.4.1
# model and tokenizer
base_model: microsoft/Phi-3-mini-4k-instruct # change for model
trust_remote_code: true
sequence_len: 2048
strict: false
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
bf16: auto
pad_to_sequence_len: true
save_safetensors: true
datasets:
- path: verifiers-for-code/cleaned_deepseek_plans
type: completion
field: text
train_on_split: train
val_set_size: 0.05
# lora
adapter: lora
lora_r: 2048
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
- embed_tokens
- lm_head
use_rslora: true
# logging
wandb_project: valeris
wandb_name: phi3-deepseek-27k-cleanedplans-longtrain
output_dir: ./outputs/phi3-deepseek-27k-cleanedplans-longtrain
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
micro_batch_size: 1
num_epochs: 3
eval_batch_size: 1
warmup_ratio: 0.05
learning_rate: 5e-6
lr_scheduler: cosine
optimizer: adamw_torch
hub_model_id: verifiers-for-code/phi3-deepseek-27k-cleanedplans-longtrain
push_to_hub: true
hub_always_push: true
evals_per_epoch: 8
saves_per_epoch: 4
logging_steps: 1
# eval_table_size: 10
# eval_max_new_tokens: 512
tokens: ["<thinking>", "</thinking>", "<plan>", "</plan>"]
special_tokens:
pad_token: "<|endoftext|>"
phi3-deepseek-27k-cleanedplans-longtrain
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3618
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 242
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.5446 | 0.0006 | 1 | 0.4877 |
0.4894 | 0.1255 | 203 | 0.4453 |
0.4329 | 0.2509 | 406 | 0.3950 |
0.4223 | 0.3764 | 609 | 0.3772 |
0.3909 | 0.5019 | 812 | 0.3705 |
0.3837 | 0.6273 | 1015 | 0.3676 |
0.3959 | 0.7528 | 1218 | 0.3658 |
0.3516 | 0.8782 | 1421 | 0.3642 |
0.3757 | 1.0037 | 1624 | 0.3632 |
0.3222 | 1.1292 | 1827 | 0.3627 |
0.3095 | 1.2546 | 2030 | 0.3624 |
0.3234 | 1.3801 | 2233 | 0.3621 |
0.3776 | 1.5056 | 2436 | 0.3620 |
0.3471 | 1.6310 | 2639 | 0.3618 |
0.343 | 1.7565 | 2842 | 0.3617 |
0.3898 | 1.8820 | 3045 | 0.3618 |
0.3207 | 2.0074 | 3248 | 0.3618 |
0.3486 | 2.1329 | 3451 | 0.3618 |
0.3362 | 2.2583 | 3654 | 0.3618 |
0.3444 | 2.3838 | 3857 | 0.3618 |
0.3717 | 2.5093 | 4060 | 0.3618 |
0.3482 | 2.6347 | 4263 | 0.3618 |
0.3393 | 2.7602 | 4466 | 0.3617 |
0.3121 | 2.8857 | 4669 | 0.3618 |
Framework versions
- PEFT 0.11.1
- Transformers 4.44.0.dev0
- Pytorch 2.4.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for verifiers-for-code/phi3-deepseek-27k-cleanedplans-longtrain
Base model
microsoft/Phi-3-mini-4k-instruct