---
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: mistral-7B-v0.1-open-orca-only-mask-0.6
  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: `0.5.2`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: AutoModelForCausalLM
tokenizer_config: Open-Orca/Mistral-7B-OpenOrca
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
resize_token_embeddings_to_32x: false

flash_attention: true
xformers_attention:

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: skymizer/open-orca-conversations
    type: chat_template
    field_messages: messages
    train_on_split: train

test_datasets:
  - path: skymizer/open-orca-conversations
    type: chat_template
    field_messages: messages
    split: test

hf_use_auth_token: true
dataset_prepared_path: /mnt/home/model-team/dataset/pretokenized/mistral-open-orca
output_dir: /mnt/home/model-team/models/mistral-7B-v0.1-open-orca-only-mask-0.6

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

eval_sample_packing: false
# eval_causal_lm_metrics: ["perplexity"]

wandb_project: "axolotl_q_sparse_sft"
wandb_entity:
wandb_watch:
wandb_name: "mistral-7B-v0.1-open-orca-only-mask-0.6"
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 8
eval_batch_size: 
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.000005
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 0.000001
max_grad_norm: 1.0

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

hub_model_id: "skymizer/mistral-7B-v0.1-open-orca-only-mask-0.6"

save_strategy: "steps"
save_steps: 500

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1

warmup_ratio: 0.03
eval_steps: 500
eval_table_size:
eval_max_new_tokens: 2048
debug:
deepspeed: /root/train/axolotl/deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
seed: 42

```

</details><br>

# mistral-7B-v0.1-open-orca-only-mask-0.6

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4523

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 182
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2681        | 0.0002 | 1    | 1.2792          |
| 0.5346        | 0.0824 | 500  | 0.5408          |
| 0.5564        | 0.1648 | 1000 | 0.5230          |
| 0.535         | 0.2472 | 1500 | 0.5106          |
| 0.5428        | 0.3296 | 2000 | 0.4996          |
| 0.5079        | 0.4120 | 2500 | 0.4903          |
| 0.4997        | 0.4944 | 3000 | 0.4803          |
| 0.5205        | 0.5768 | 3500 | 0.4718          |
| 0.4748        | 0.6592 | 4000 | 0.4645          |
| 0.4935        | 0.7416 | 4500 | 0.4588          |
| 0.5065        | 0.8240 | 5000 | 0.4548          |
| 0.4481        | 0.9064 | 5500 | 0.4528          |
| 0.4774        | 0.9888 | 6000 | 0.4523          |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3