
**Note: internal model, not ready for use**

This is an intermediate model used as base-model for further pythia 12b SFT-8 experiments. 
It was trained on a wider set of instruction-tuning datasets for >12.5k steps with batch-size 128 and a context size of 2048.
The gpt4all dataset had "as a language model" *contamination* (>1.8k entries). We added filtering later, but this model (pre-v8) was trained on the raw unfildered gpt4all dataset.


- wandb: https://wandb.ai/open-assistant/supervised-finetuning/runs/sytsyhrp
- [sampling report](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-pretrained%2F2023-05-05_OpenAssistant_pythia-12b-pre-v8-12_5k-steps_sampling_noprefix2.json)


Datasets: 

```
pretrain:
  num_train_epochs: 1
  weight_decay: 0.0
  use_custom_sampler: true
  sort_by_length: false
  datasets:
    - gpteacher_roleplay:
        val_split: 0.05
    - red_pajama:
        fraction: 0.25
        max_val_set: 1000
    - wizardlm_70k:
        val_split: 0.05
        max_val_set: 500
    - joke:
        val_split: 0.05
    - poem_instructions:
        val_split: 0.025
    - oa_stackexchange:
        val_split: 0.05
        fraction: 0.1
        max_val_set: 1000
    - tell_a_joke:
        val_split: 0.05
        max_val_set: 250
    - webgpt:
        val_split: 0.05
        max_val_set: 250
    - gpt4all:
        val_split: 0.01
        max_val_set: 1000
    - alpaca_gpt4:
        val_split: 0.025
        max_val_set: 250
    - code_alpaca:
        val_split: 0.05
        max_val_set: 250
    - vicuna:
        max_val_set: 250
    - oig_file:
        source_url: https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl
        max_count: 10000
        min_length: 250
        val_split: 0.05
        max_val_set: 250
    - minimath:
        val_split: 0.05
    - humaneval_mbpp_codegen_qa:
        val_split: 0.05
    - humaneval_mbpp_testgen_qa:
        val_split: 0.05
    - grade_school_math_instructions:
        val_split: 0.05
    - recipes:
        val_split: 0.05
    - cmu_wiki_qa:
        val_split: 0.05
 - oa_wiki_qa_bart_10000row:
        val_split: 0.05
        max_val_set: 250
    - prosocial_dialogue:
        fraction: 0.1
        max_val_set: 250
    - explain_prosocial:
        fraction: 0.075
        max_val_set: 250
    - soda:
        fraction: 0.25
        max_val_set: 1000
    - oa_leet10k:
        val_split: 0.05
        max_val_set: 250
    - dolly15k:
        val_split: 0.05
        max_val_set: 300
```


Pythia:
```
pythia-12b-pretrain:
  dtype: fp16
  log_dir: "pythia_log_12b"
  learning_rate: 6e-6
  model_name: EleutherAI/pythia-12b-deduped
  output_dir: pythia_model_12b
  weight_decay: 0.0
  max_length: 2048
  warmup_steps: 100
  gradient_checkpointing: true
  gradient_accumulation_steps: 4
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 4
  eval_steps: 251
  save_steps: 500
  num_train_epochs: 1
  save_total_limit: 2
  deepspeed_config: configs/zero_config_pretrain.json
```


Command used: `deepspeed trainer_sft.py --show_dataset_stats --configs defaults pythia-12b-pretrain pretrain --cache_dir .cache/ --output_dir .saved/pythia-12b-super-pretrain2 --deepspeed`
{"license": "apache-2.0"}
transformers pytorch gpt_neox text-generation license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


Note: internal model, not ready for use

This is an intermediate model used as base-model for further pythia 12b SFT-8 experiments. 
It was trained on a wider set of instruction-tuning datasets for >12.5k steps with batch-size 128 and a context size of 2048.
The gpt4all dataset had "as a language model" *contamination* (>1.8k entries). We added filtering later, but this model (pre-v8) was trained on the raw unfildered gpt4all dataset.


- wandb: URL
- sampling report


Datasets: 




Pythia:



Command used: 'deepspeed trainer_sft.py --show_dataset_stats --configs defaults pythia-12b-pretrain pretrain --cache_dir .cache/ --output_dir .saved/pythia-12b-super-pretrain2 --deepspeed'
TAGS
#transformers #pytorch #gpt_neox #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
