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

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: json
    data_files: plain_qa_list.jsonl
    ds_type: json
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/UFOs-Finetune-V1/out

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

max_steps: 100000

wandb_project: "UFO_LLM_Finetune"
wandb_entity:
wandb_watch: "all"
wandb_name: "UFO_LLM_Finetune-V1"
wandb_log_model: "false"

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: /workspace/axolotl/outputs/out/checkpoint-18
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-Finetune-V1

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the json dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3935

## 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: 50

### 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          |
| 2.8515        | 4.1739 | 21   | 1.4353          |
| 1.3702        | 4.6957 | 24   | 1.4068          |
| 1.2889        | 5.1739 | 27   | 1.3909          |
| 1.2635        | 5.6957 | 30   | 1.3870          |
| 1.2139        | 6.1739 | 33   | 1.3874          |
| 1.1786        | 6.6957 | 36   | 1.3895          |
| 1.1458        | 7.1739 | 39   | 1.3921          |
| 1.1389        | 7.6957 | 42   | 1.3929          |
| 1.1255        | 8.1739 | 45   | 1.3934          |
| 1.1589        | 8.6957 | 48   | 1.3935          |


### Framework versions

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