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---
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: "Instruction์ ๋ฐ๋ผ ์ ์ ํ๊ฒ Input ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ Output ๋ต๋ณ์ ํ์ธ์. ๋๋ ์ฌ์ฉ์ ์ง๋ฌธ(Instruction)์ ์ค์๊ฐ์ผ๋ก API ํธ์ถ์ ์ํ Json ํ์์ ๊ตฌ์กฐํ๋ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํ๋ ์ธ๊ณต์ง๋ฅ์ด์ผ."
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 |