---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-8B-Base
tags:
- generated_from_trainer
datasets:
- allura-org/inkmix-v3.0
model-index:
- name: ephemeral/ckpts
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.10.0.dev0`
```yaml
# === Model Configuration ===
base_model: Qwen/Qwen3-8B-Base
load_in_8bit: false
load_in_4bit: false
# === Training Setup ===
num_epochs: 2
micro_batch_size: 32
gradient_accumulation_steps: 1
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
# === Hyperparameter Configuration ===
optimizer: apollo_adamw_layerwise
# Apollo-mini configuration:
optim_args: "proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200"
# Regular Apollo configuration:
# optim_args:
optim_target_modules: all_linear
learning_rate: 2e-5
lr_scheduler: rex
weight_decay: 0.01
warmup_ratio: 0
# === Data Configuration ===
datasets:
- path: allura-org/inkmix-v3.0
type: chat_template
split: train
field_messages: conversations
message_field_role: from
message_field_content: value
dataset_prepared_path: last_run_prepared
chat_template: chatml
# === Plugins ===
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
# === Hardware Optimization ===
gradient_checkpointing: unsloth
gradient_checkpointing_kwargs:
use_reentrant: false
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
cut_cross_entropy: true
# === Wandb Tracking ===
wandb_project: qwen3-8b-inkmix-v3
# === Checkpointing ===
saves_per_epoch: 2
save_total_limit: 3
# === Advanced Settings ===
output_dir: /ephemeral/ckpts
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
logging_steps: 1
trust_remote_code: true
```
# ephemeral/ckpts
This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on the allura-org/inkmix-v3.0 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use apollo_adamw_layerwise with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200
- lr_scheduler_type: cosine
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1