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---
license: apache-2.0
base_model: Afterparty-hf/pretrain-0.924
tags:
- axolotl
- generated_from_trainer
model-index:
- name: finetune-0.559
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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: Afterparty-hf/pretrain-0.924
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Afterparty-hf/synthetic-instruct
type: sharegpt
- path: Afterparty-hf/train-format-server
type: sharegpt
- path: Afterparty-hf/help-channels-formatted
type: sharegpt
- path: Afterparty-hf/constt-augmented
type: sharegpt
- path: Afterparty-hf/transcripts-train
type: sharegpt
chat_template: chatml
dataset_prepared_path: ./prepath
hub_model_id: Afterparty-hf/finetune-0.559
wandb_project: ap_publi
hf_use_auth_token: true
output_dir: ./finetune-559-a
resume_from_checkpoint: ./finetune-559/checkpoint-1026
wandb_watch: all
hub_private_repo: true
hub_strategy: all_checkpoints
push_to_hub: false
hf_use_auth_token: true
max_grad_norm: 0.6
sequence_len: 14256
sample_packing: true
pad_to_sequence_len: true
micro_batch_size: 1
gradient_accumulation_steps: 1
num_epochs: 4
learning_rate: 0.000004
optimizer: adamw_bnb_8bit
#optim_args:
# amsgrad: true
lr_scheduler: cosine
train_on_inputs: false
group_by_length: false
bfloat16: false
#bf16: auto
fp16:
tf32: false
neftune_noise_alpha: 2
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
xformers_attention:
flash_attention: true
#unsloth_lora_mlp: true
#unsloth_lora_qkv: true
#unsloth_lora_o: true
#flash_attn_cross_entropy: true
#flash_attn_rms_norm: true
#flash_attn_fuse_qkv: false
#flash_attn_fuse_mlp: true
warmup_ratio: 0.5
evals_per_step: 0.025
eval_table_size:
saves_per_epoch: 5
debug:
torch_compile: true
rank:
deepspeed: deepspeed_configs/zero2.json
save_safetensors: true
weight_decay: 0.01
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
pad_token: "</s>"
tokens: # these are delimiters
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
# finetune-0.559
This model is a fine-tuned version of [Afterparty-hf/pretrain-0.924](https://huggingface.co/Afterparty-hf/pretrain-0.924) on the None 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: 4e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- 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: 310
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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