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---
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 |