---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: NistCodeLlama-7b
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: NistCodeLlama-7b
sample_packing: false
eval_sample_packing: false
load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: rkreddyp/nist_800_53
    ds_type: json
    type:
              field_instruction: question
              field_input: context
              field_output: answer
              format: |-
                [INST] Using the schema context below, generate a SQL query that answers the question.
                {input}
                {instruction} [/INST]   


dataset_prepared_path:
val_set_size: 0.02
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: axolotl-nist
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# NistCodeLlama-7b

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: 0.3414

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4855        | 0.06  | 1    | 1.4808          |
| 1.4522        | 0.11  | 2    | 1.4811          |
| 1.4616        | 0.17  | 3    | 1.4788          |
| 1.5276        | 0.23  | 4    | 1.4746          |
| 1.4564        | 0.29  | 5    | 1.4662          |
| 1.4837        | 0.34  | 6    | 1.4515          |
| 1.4709        | 0.4   | 7    | 1.4280          |
| 1.3571        | 0.46  | 8    | 1.3903          |
| 1.4164        | 0.51  | 9    | 1.3363          |
| 1.3257        | 0.57  | 10   | 1.2692          |
| 1.2858        | 0.63  | 11   | 1.2027          |
| 1.2318        | 0.69  | 12   | 1.1364          |
| 1.1164        | 0.74  | 13   | 1.0595          |
| 1.0984        | 0.8   | 14   | 0.9748          |
| 0.9593        | 0.86  | 15   | 0.8923          |
| 0.8325        | 0.91  | 16   | 0.8137          |
| 0.8357        | 0.97  | 17   | 0.7426          |
| 0.6483        | 1.03  | 18   | 0.6868          |
| 0.7138        | 1.06  | 19   | 0.6400          |
| 0.6105        | 1.11  | 20   | 0.6027          |
| 0.6409        | 1.17  | 21   | 0.5686          |
| 0.5206        | 1.23  | 22   | 0.5317          |
| 0.521         | 1.29  | 23   | 0.4962          |
| 0.4409        | 1.34  | 24   | 0.4697          |
| 0.4678        | 1.4   | 25   | 0.4481          |
| 0.3731        | 1.46  | 26   | 0.4303          |
| 0.388         | 1.51  | 27   | 0.4161          |
| 0.3463        | 1.57  | 28   | 0.4085          |
| 0.3699        | 1.63  | 29   | 0.4035          |
| 0.3673        | 1.69  | 30   | 0.3992          |
| 0.4485        | 1.74  | 31   | 0.3962          |
| 0.3855        | 1.8   | 32   | 0.3929          |
| 0.3249        | 1.86  | 33   | 0.3887          |
| 0.3528        | 1.91  | 34   | 0.3839          |
| 0.372         | 1.97  | 35   | 0.3801          |
| 0.3922        | 2.03  | 36   | 0.3768          |
| 0.3783        | 2.06  | 37   | 0.3739          |
| 0.31          | 2.11  | 38   | 0.3721          |
| 0.275         | 2.17  | 39   | 0.3699          |
| 0.338         | 2.23  | 40   | 0.3665          |
| 0.3238        | 2.29  | 41   | 0.3633          |
| 0.3382        | 2.34  | 42   | 0.3597          |
| 0.3467        | 2.4   | 43   | 0.3567          |
| 0.3494        | 2.46  | 44   | 0.3541          |
| 0.3431        | 2.51  | 45   | 0.3533          |
| 0.3433        | 2.57  | 46   | 0.3522          |
| 0.304         | 2.63  | 47   | 0.3491          |
| 0.3098        | 2.69  | 48   | 0.3464          |
| 0.279         | 2.74  | 49   | 0.3443          |
| 0.3105        | 2.8   | 50   | 0.3425          |
| 0.2305        | 2.86  | 51   | 0.3414          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0