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mistral-7B-v0.1-relufication-stage-1-on-slim-orca

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mistral-7B-v0.1-relufication-stage-1-on-slim-orca
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---
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-relufication-stage-1-on-slim-orca
  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/Sonnet3.5-SlimOrcaDedupCleaned-train
    type: chat_template
    field_messages: messages

test_datasets:
  - path: skymizer/Sonnet3.5-SlimOrcaDedupCleaned-test
    type: chat_template
    field_messages: messages
    split: train

hf_use_auth_token: true
dataset_prepared_path: pretokenized/slim-orca
output_dir: ./exp_output_artifacts

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

eval_sample_packing: false
# eval_causal_lm_metrics: ["perplexity"]

wandb_project: "axolotl_mistral_sft"
wandb_entity:
wandb_watch:
wandb_name: "mistral-7B-v0.1-relufication-stage-1-on-slim-orca"
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 16
eval_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.000005
warmup_ratio: 0.03
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-relufication-stage-1-on-slim-orca"

save_strategy: "steps"
save_steps: 50

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

eval_steps: 50
eval_table_size:
eval_max_new_tokens: 2048
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
seed: 42

```

</details><br>

# mistral-7B-v0.1-relufication-stage-1-on-slim-orca

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: 0.6542

## 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: 16
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 4
- 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: 11
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.6459       | 0.0026 | 1    | 10.7631         |
| 1.075         | 0.1277 | 50   | 1.0591          |
| 0.7996        | 0.2554 | 100  | 0.7829          |
| 0.7357        | 0.3831 | 150  | 0.7247          |
| 0.7094        | 0.5109 | 200  | 0.6953          |
| 0.6835        | 0.6386 | 250  | 0.6727          |
| 0.691         | 0.7663 | 300  | 0.6603          |
| 0.6723        | 0.8940 | 350  | 0.6542          |


### Framework versions

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