---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- axolotl
- generated_from_trainer
datasets:
- AiAF/Codename-75567-Pretrainin.jsonl
model-index:
- name: Pretrained-QLoRA-Codename-75567-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: `0.8.0.dev0`
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.3
# 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/Pretrained-QLoRA-Codename-75567-V1

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: AiAF/Codename-75567-Pretrainin.jsonl
    type: completion

dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/qlora-out
save_total_limit: 10

adapter: qlora
lora_model_dir:
lora_r: 256
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

sequence_len: 512
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: "LLM-Pretraining"
wandb_watch: "all"
wandb_name: "QLoRA-Codename-75567-V1"
wandb_log_model: "false"
wandb_run_id: "QLoRA-Codename-75567-V1"

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
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: 1
evals_per_epoch: 5
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>

# Pretrained-QLoRA-Codename-75567-V1

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the AiAF/Codename-75567-Pretrainin.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6938

## 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: 2
- num_epochs: 10.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8916        | 0.3333 | 1    | 1.8880          |
| 2.017         | 0.6667 | 2    | 1.8847          |
| 1.9119        | 1.0    | 3    | 1.8795          |
| 1.9716        | 1.3333 | 4    | 1.8711          |
| 1.8532        | 1.6667 | 5    | 1.8601          |
| 1.9759        | 2.0    | 6    | 1.8488          |
| 1.856         | 2.3333 | 7    | 1.8357          |
| 1.8404        | 2.6667 | 8    | 1.8241          |
| 1.976         | 3.0    | 9    | 1.8131          |
| 1.8504        | 3.3333 | 10   | 1.8012          |
| 1.8574        | 3.6667 | 11   | 1.7860          |
| 1.8194        | 4.0    | 12   | 1.7749          |
| 1.8022        | 4.3333 | 13   | 1.7646          |
| 1.7632        | 4.6667 | 14   | 1.7525          |
| 1.8326        | 5.0    | 15   | 1.7440          |
| 1.7696        | 5.3333 | 16   | 1.7325          |
| 1.8039        | 5.6667 | 17   | 1.7257          |
| 1.7019        | 6.0    | 18   | 1.7164          |
| 1.7878        | 6.3333 | 19   | 1.7132          |
| 1.718         | 6.6667 | 20   | 1.7093          |
| 1.6994        | 7.0    | 21   | 1.7049          |
| 1.785         | 7.3333 | 22   | 1.6996          |
| 1.6659        | 7.6667 | 23   | 1.6977          |
| 1.7241        | 8.0    | 24   | 1.6970          |
| 1.7397        | 8.3333 | 25   | 1.6952          |
| 1.6894        | 8.6667 | 26   | 1.6934          |
| 1.723         | 9.0    | 27   | 1.6932          |
| 1.7999        | 9.3333 | 28   | 1.6927          |
| 1.6715        | 9.6667 | 29   | 1.6941          |
| 1.6696        | 10.0   | 30   | 1.6938          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0