---
library_name: peft
tags:
- axolotl
- generated_from_trainer
- text-generation-inference
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: logic_magazine_jsonl
  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: `0.3.0`
```yaml
# Image: winglian/axolotl:main-py3.10-cu118-2.0.1 
base_model: meta-llama/Llama-2-7b-hf
base_model_config: meta-llama/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: bentarnoff/logic_magazine_jsonl
    type: sharegpt
hub_model_id: bentarnoff/logic_magazine_jsonl
val_set_size: 0.01
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: 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: "logic_magazine"
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model: "checkpoint"

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: 10
eval_steps: 20
eval_table_size: 5
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

</details><br>

# logic_magazine_jsonl

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3642

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3553        | 0.15  | 20   | 2.4167          |
| 2.2767        | 0.31  | 40   | 2.3869          |
| 2.2854        | 0.46  | 60   | 2.3658          |
| 2.2849        | 0.61  | 80   | 2.3470          |
| 2.353         | 0.76  | 100  | 2.3337          |
| 2.2412        | 0.92  | 120  | 2.3363          |
| 2.1992        | 1.07  | 140  | 2.3240          |
| 2.1069        | 1.22  | 160  | 2.3404          |
| 2.2444        | 1.37  | 180  | 2.3403          |
| 2.1424        | 1.53  | 200  | 2.3446          |
| 2.1739        | 1.68  | 220  | 2.3404          |
| 2.1423        | 1.83  | 240  | 2.3382          |
| 2.1721        | 1.98  | 260  | 2.3378          |
| 2.1621        | 2.14  | 280  | 2.3630          |
| 2.0394        | 2.29  | 300  | 2.3623          |
| 2.0631        | 2.44  | 320  | 2.3665          |
| 2.0234        | 2.6   | 340  | 2.3632          |
| 2.1042        | 2.75  | 360  | 2.3654          |
| 2.02          | 2.9   | 380  | 2.3642          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0