---
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- axolotl
- generated_from_trainer
datasets:
- AiAF/pretraining.jsonl
model-index:
- name: UFOs-Pretraining-V1
  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/UFOs-Pretraining-V1

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AiAF/pretraining.jsonl
    type: completion

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/out

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

max_steps: 100000

wandb_project: "UFO_LLM_Pretraining"
wandb_entity:
wandb_watch: "all"
wandb_name: "UFO_LLM_Pretraining-V1.0"
wandb_log_model: "false"

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
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:
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>

# UFOs-Pretraining-V1

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the AiAF/pretraining.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6503

## 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
- training_steps: 36

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7686        | 0.1111 | 1    | 1.6895          |
| 2.0582        | 0.3333 | 3    | 1.6884          |
| 1.9135        | 0.6667 | 6    | 1.6793          |
| 1.8261        | 1.0    | 9    | 1.6667          |
| 1.8757        | 1.3333 | 12   | 1.6570          |
| 1.8754        | 1.6667 | 15   | 1.6501          |
| 1.8426        | 2.0    | 18   | 1.6468          |
| 1.7298        | 2.3333 | 21   | 1.6536          |
| 1.4421        | 2.6667 | 24   | 1.6518          |
| 1.7533        | 3.0    | 27   | 1.6490          |
| 1.5505        | 3.3333 | 30   | 1.6498          |
| 1.4142        | 3.6667 | 33   | 1.6500          |
| 1.6097        | 4.0    | 36   | 1.6503          |


### Framework versions

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