---
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- axolotl
- generated_from_trainer
datasets:
- AiAF/KJV-LLM-pretraining.jsonl
model-index:
- name: KJV-LLM-Pretrained-V1.1
  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: `0.6.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
# optionally might have model_type or tokenizer_type
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
hub_model_id: AiAF/KJV-LLM-Pretrained-V1.1

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AiAF/KJV-LLM-pretraining.jsonl
    type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/out/KJV-LLM-Pretrained-V1.1

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: "LLM-Pretraining"
wandb_entity:
wandb_watch: "all"
wandb_name: "KJV-LLM-Pretrained-V1.1"
wandb_log_model: "false"

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 8
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: /workspace/axolotl/outputs/out/KJV-LLM-Pretrained-V1.0/checkpoint-28
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# KJV-LLM-Pretrained-V1.1

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the AiAF/KJV-LLM-pretraining.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0986

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- num_epochs: 8.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0809        | 0.1333 | 1    | 0.0975          |
| 0.0878        | 0.2667 | 2    | 0.0963          |
| 0.3153        | 0.5333 | 4    | 0.0909          |
| 0.077         | 0.8    | 6    | 0.0854          |
| 0.2377        | 1.0    | 8    | 0.0820          |
| 0.0509        | 1.2667 | 10   | 0.0858          |
| 0.0429        | 1.5333 | 12   | 0.0862          |
| 0.3496        | 1.8    | 14   | 0.0872          |
| 0.0426        | 2.0    | 16   | 0.0895          |
| 0.0337        | 2.2667 | 18   | 0.0888          |
| 0.0348        | 2.5333 | 20   | 0.0905          |
| 0.0852        | 2.8    | 22   | 0.0902          |
| 0.0317        | 3.0    | 24   | 0.0902          |
| 0.0304        | 3.2667 | 26   | 0.0900          |
| 0.0242        | 3.5333 | 28   | 0.0901          |
| 0.1936        | 4.2667 | 30   | 0.0918          |
| 0.0242        | 4.5333 | 32   | 0.0960          |
| 0.0219        | 4.8    | 34   | 0.0940          |
| 0.0187        | 5.0    | 36   | 0.0953          |
| 0.0188        | 5.2667 | 38   | 0.0954          |
| 0.0158        | 5.5333 | 40   | 0.0966          |
| 0.3393        | 5.8    | 42   | 0.0979          |
| 0.0163        | 6.0    | 44   | 0.0984          |
| 0.3313        | 6.2667 | 46   | 0.0984          |
| 0.015         | 6.5333 | 48   | 0.0985          |
| 0.0168        | 6.8    | 50   | 0.0986          |
| 0.0144        | 7.0    | 52   | 0.0986          |
| 0.0147        | 7.2667 | 54   | 0.0987          |
| 0.0154        | 7.5333 | 56   | 0.0986          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0