---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ
model-index:
- name: output_solor/exp_16
  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: TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ
is_llama_derived_model: false
gptq: true
gptq_disable_exllama: true
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
tokenizer_use_fast: true
tokenizer_legacy: true
load_in_8bit: false
load_in_4bit: false
strict: false
push_dataset_to_hub:
hf_use_auth_token: true
datasets:
  - path:  datasets_cleansinng/datasets/helper_selector_1280_0305_v01.jsonl #Path to json dataset file in huggingface
    #for type,conversation arguments read axolotl readme and pick what is suited for your project, I wanted a chatbot and put sharegpt and chatml
    type: 
      system_prompt: "Instruction에 따라 적절하게 Input 데이터를 활용하여 Output 답변을 하세요. 너는 사용자 질문(Instruction)에 실시간으로 API 호출을 위한 Json 형식의 구조화된 결과를 생성하는 인공지능이야."
      format: "[INST]### Instruction:\n{instruction}\n\n### Input:{input}\n\n[/INST]### Output: "
      no_input_format: "[INST]### Instruction:\n{instruction}\n\n[/INST]### Output: "
      field_instruction: Instruction 
      field_input: Input
      field_output: Output
dataset_prepared_path:
val_set_size: 0.05
adapter: lora
lora_model_dir:
sequence_len: 4096
sample_packing:
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - k_proj
  - o_proj
  - q_proj
  - v_proj
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./output_solor/exp_16
gradient_accumulation_steps: 8
micro_batch_size: 8
num_epochs: 5
optimizer: adamw_torch
adam_beta2: 0.95
adam_eps: 0.00001
max_grad_norm: 1.0
torchdistx_path:
lr_scheduler: cosine
lr_quadratic_warmup: true
learning_rate: 0.0005
train_on_inputs: false
group_by_length: false
bf16: false
fp16: false
float16: true
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
sdp_attention:
flash_optimum:
warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.1
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# output_solor/exp_16

This model is a fine-tuned version of [TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2015

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3493        | 0.05  | 1    | 1.2795          |
| 1.2483        | 0.26  | 5    | 1.2769          |
| 1.2275        | 0.53  | 10   | 1.2099          |
| 1.0529        | 0.79  | 15   | 1.0724          |
| 0.8642        | 1.05  | 20   | 0.9709          |
| 0.8477        | 1.32  | 25   | 0.8245          |
| 0.7207        | 1.58  | 30   | 0.6994          |
| 0.4656        | 1.84  | 35   | 0.5878          |
| 0.4949        | 2.11  | 40   | 0.4970          |
| 0.3497        | 2.37  | 45   | 0.4221          |
| 0.3288        | 2.63  | 50   | 0.3672          |
| 0.3011        | 2.89  | 55   | 0.3250          |
| 0.2648        | 3.16  | 60   | 0.2900          |
| 0.3084        | 3.42  | 65   | 0.2591          |
| 0.2696        | 3.68  | 70   | 0.2459          |
| 0.2197        | 3.95  | 75   | 0.2286          |
| 0.1905        | 4.21  | 80   | 0.2111          |
| 0.1815        | 4.47  | 85   | 0.2084          |
| 0.2164        | 4.74  | 90   | 0.2128          |
| 0.1412        | 5.0   | 95   | 0.2015          |


### Framework versions

- PEFT 0.9.1.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.0