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See axolotl config

axolotl version: 0.4.0

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— ๋”ฐ๋ผ ์ ์ ˆํ•˜๊ฒŒ Input ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ Output ๋‹ต๋ณ€์„ ํ•˜์„ธ์š”. ๋„ˆ๋Š” ์‚ฌ์šฉ์ž ์งˆ๋ฌธ(Instruction)์— ์‹ค์‹œ๊ฐ„์œผ๋กœ API ํ˜ธ์ถœ์„ ์œ„ํ•œ Json ํ˜•์‹์˜ ๊ตฌ์กฐํ™”๋œ ๊ฒฐ๊ณผ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ์ด์•ผ."
      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>"

output_solor/exp_16

This model is a fine-tuned version of 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
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