File size: 3,302 Bytes
0795f92 |
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 |
---
library_name: peft
license: apache-2.0
base_model: allura-org/Teleut-7b
tags:
- generated_from_trainer
datasets:
- allura-org/neon-41k
model-index:
- name: ckpts
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. -->
[<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 config</summary>
axolotl version: `0.6.0`
```yaml
base_model: allura-org/Teleut-7b
load_in_8bit: true
load_in_4bit: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
#unsloth_lora_mlp: true
#unsloth_lora_qkv: true
#unsloth_lora_o: true
strict: false
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.25
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
lora_target_linear: true
peft_layers_to_transform:
loraplus_lr_ratio: 16
chat_template: chatml
datasets:
- path: allura-org/neon-41k
type: chat_template
split: train
field_messages: conversations
message_field_role: from
message_field_content: value
dataset_prepared_path: last_run_prepared
#val_set_size: 0.02
output_dir: ./ckpts
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
#wandb_project: teleut-7b-rp
#wandb_entity:
#wandb_watch:
#wandb_name:
#wandb_log_model: checkpoint
# mlflow configuration if you're using it
mlflow_tracking_uri: https://public-tracking.mlflow-e00zzfjq11ky6jcgtv.backbone-e00bgn6e63256prmhq.msp.eu-north1.nebius.cloud
mlflow_experiment_name: teleut-7b-rp
mlflow_run_name: v1
hf_mlflow_log_artifacts: true
gradient_accumulation_steps: 1
micro_batch_size: 12
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 6e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: unsloth
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
#deepspeed: deepspeed_configs/zero3_bf16.json
warmup_steps: 25
#evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 25
debug:
weight_decay: 0.01
```
</details><br>
# ckpts
This model is a fine-tuned version of [allura-org/Teleut-7b](https://huggingface.co/allura-org/Teleut-7b) on the allura-org/neon-41k dataset.
## 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: 6e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Use 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: 25
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0 |