Lambent / cosmo-upscale-lisa

huggingface.co
Total runs: 6
24-hour runs: 0
7-day runs: -1
30-day runs: 0
Model's Last Updated: April 10 2024
text-generation

Introduction of cosmo-upscale-lisa

Model Details of cosmo-upscale-lisa

Tried depth-upscaling Cosmo-1b by duplicating 6 layers, then LISA-training on a dataset reasonably similar to the original one in an attempt to 'self-repair'.

Not sure if it worked out exactly how I pictured but the nous eval's not overall much worse than the original at least.

(Took I think about 8 hours for, I want to say, ~80 million tokens on one RTX 3090?)

Thought about doing LORA first but I couldn't get peft_layers_to_transform working on axolotl and decided to go straight to LISA.

It's probably good(?) for (random selection of layers) to get experience trying to work around (random thick doubled layers) in some kind of brain-exercise sense anyway.

Capabilities assessment vs original and upscaled version:

Model AGIEval GPT4All TruthfulQA Bigbench Average
cosmo-upscale-lisa 22.76 49.69 39.49 28.7 35.16
Model AGIEval GPT4All TruthfulQA Bigbench Average
cosmo-1b 22.97 52.01 38.02 28.73 35.43
Model AGIEval GPT4All TruthfulQA Bigbench Average
cosmo-upscale 22.23 48.35 42.01 28.36 35.24

I'm not sure this helped.

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: Lambent/cosmo-upscale
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: HuggingFaceTB/cosmopedia-100k
    type: completion
  - path: Vezora/Tested-22k-Python-Alpaca
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lisa-out

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

lisa_n_layers: 4
lisa_step_interval: 10
lisa_layers_attribute: model.layers

wandb_project: cosmouplisa
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

lisa-out

This model is a fine-tuned version of Lambent/cosmo-upscale on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0353
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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_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: 1
Training results
Training Loss Epoch Step Validation Loss
1.4298 0.0 1 1.4591
1.1229 0.25 1480 1.0594
1.0711 0.5 2960 1.0418
1.0511 0.75 4440 1.0353
Framework versions
  • Transformers 4.40.0.dev0
  • Pytorch 2.1.2+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.0

Runs of Lambent cosmo-upscale-lisa on huggingface.co

6
Total runs
0
24-hour runs
-1
3-day runs
-1
7-day runs
0
30-day runs

More Information About cosmo-upscale-lisa huggingface.co Model

cosmo-upscale-lisa huggingface.co

cosmo-upscale-lisa huggingface.co is an AI model on huggingface.co that provides cosmo-upscale-lisa's model effect (), which can be used instantly with this Lambent cosmo-upscale-lisa model. huggingface.co supports a free trial of the cosmo-upscale-lisa model, and also provides paid use of the cosmo-upscale-lisa. Support call cosmo-upscale-lisa model through api, including Node.js, Python, http.

cosmo-upscale-lisa huggingface.co Url

https://huggingface.co/Lambent/cosmo-upscale-lisa

Lambent cosmo-upscale-lisa online free

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https://huggingface.co/Lambent/cosmo-upscale-lisa

cosmo-upscale-lisa install

cosmo-upscale-lisa is an open source model from GitHub that offers a free installation service, and any user can find cosmo-upscale-lisa on GitHub to install. At the same time, huggingface.co provides the effect of cosmo-upscale-lisa install, users can directly use cosmo-upscale-lisa installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

cosmo-upscale-lisa install url in huggingface.co:

https://huggingface.co/Lambent/cosmo-upscale-lisa

Url of cosmo-upscale-lisa

cosmo-upscale-lisa huggingface.co Url

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