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library_name: transformers
license: other
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
tags:
- generated_from_trainer
datasets:
- axolotl_format_deepseek_combined_wm.json
model-index:
- name: models/deepseek_wm
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: `0.5.3.dev44+g5bef1906`
```yaml
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
trust_remote_code: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
datasets:
- path: axolotl_format_deepseek_combined_wm.json
type: input_output
dataset_prepared_path: last_run_prepared_deepseek
output_dir: ./models/deepseek_wm
sequence_len: 4096
wandb_project: agent-v0
wandb_name: deepseek_wm
train_on_inputs: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch
learning_rate: 2e-5
xformers_attention:
flash_attention: true
logging_steps: 5
warmup_steps: 5
saves_per_epoch: 1
weight_decay: 0.0
deepspeed: axolotl/deepspeed_configs/zero3_bf16_cpuoffload_all.json
```
</details><br>
# models/deepseek_wm
This model is a fine-tuned version of [deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) on the axolotl_format_deepseek_combined_wm.json 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
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