---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: ministral_outputs
  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: `0.4.1`
```yaml
base_model: ./prince-canuma_Ministral-8B-Instruct-2410-HF
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: NewEden/Story-writing-Alt-Data-sharegpt
    type: sharegpt
    conversation: chatml
  - path: NewEden/Misc-Phase-Sharegpt
    type: sharegpt
    conversation: chatml
  - path: NewEden/Synth-RP-Phase-sharegpt
    type: sharegpt
    conversation: chatml
  - path: NewEden/Claude-instruct-Merged-Sharegpt
    type: sharegpt
    conversation: chatml
  - path: NewEden/Intelligence-Phase-Sharegpt
    type: sharegpt
    conversation: chatml
chat_template: chatml

output_dir: ./ministral_outputs

  #adapter: lora
  #lora_r: 128
  #lora_alpha: 16
  #lora_dropout:  0.05
  #lora_target_linear: true
  #peft_use_rslora: true
  #lora_modules_to_save:
  #- embed_tokens
  #- lm_head

sequence_len: 16384
  #sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true


wandb_project: Ministral-Tor
wandb_entity:
wandb_watch:
wandb_name: run-1
wandb_log_model:


plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
#auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 30
eval_table_size:
saves_per_epoch: 1
weight_decay: 0.1
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_params.json
  #deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
  #deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<pad>"

```

</details><br>

# ministral_outputs

This model was trained from scratch 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1