Lambent / cosmo-plus-1b-v0.1

huggingface.co
Total runs: 0
24-hour runs: 0
7-day runs: 0
30-day runs: -3
Model's Last Updated: July 26 2026

Introduction of cosmo-plus-1b-v0.1

Model Details of cosmo-plus-1b-v0.1

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: HuggingFaceTB/cosmo-1b
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: Vezora/Tested-22k-Python-Alpaca
    type: alpaca
  - path: teknium/GPTeacher-General-Instruct #89.3k
    type: gpteacher
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./trained-lora-out

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 128
lora_alpha: 16
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: Trained-Cosmo-1b-v0.1
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
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:

trained-lora-out

This model is a fine-tuned version of HuggingFaceTB/cosmo-1b on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6129
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: 2
Training results
Training Loss Epoch Step Validation Loss
0.6539 1.02 1827 0.6588
0.5693 1.98 3596 0.6129
Framework versions
  • PEFT 0.9.0
  • Transformers 4.39.0.dev0
  • Pytorch 2.1.1+cu121
  • Datasets 2.17.1
  • Tokenizers 0.15.0

Runs of Lambent cosmo-plus-1b-v0.1 on huggingface.co

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24-hour runs
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3-day runs
0
7-day runs
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30-day runs

More Information About cosmo-plus-1b-v0.1 huggingface.co Model

cosmo-plus-1b-v0.1 huggingface.co

cosmo-plus-1b-v0.1 huggingface.co is an AI model on huggingface.co that provides cosmo-plus-1b-v0.1's model effect (), which can be used instantly with this Lambent cosmo-plus-1b-v0.1 model. huggingface.co supports a free trial of the cosmo-plus-1b-v0.1 model, and also provides paid use of the cosmo-plus-1b-v0.1. Support call cosmo-plus-1b-v0.1 model through api, including Node.js, Python, http.

cosmo-plus-1b-v0.1 huggingface.co Url

https://huggingface.co/Lambent/cosmo-plus-1b-v0.1

Lambent cosmo-plus-1b-v0.1 online free

cosmo-plus-1b-v0.1 huggingface.co is an online trial and call api platform, which integrates cosmo-plus-1b-v0.1's modeling effects, including api services, and provides a free online trial of cosmo-plus-1b-v0.1, you can try cosmo-plus-1b-v0.1 online for free by clicking the link below.

Lambent cosmo-plus-1b-v0.1 online free url in huggingface.co:

https://huggingface.co/Lambent/cosmo-plus-1b-v0.1

cosmo-plus-1b-v0.1 install

cosmo-plus-1b-v0.1 is an open source model from GitHub that offers a free installation service, and any user can find cosmo-plus-1b-v0.1 on GitHub to install. At the same time, huggingface.co provides the effect of cosmo-plus-1b-v0.1 install, users can directly use cosmo-plus-1b-v0.1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

cosmo-plus-1b-v0.1 install url in huggingface.co:

https://huggingface.co/Lambent/cosmo-plus-1b-v0.1

Url of cosmo-plus-1b-v0.1

cosmo-plus-1b-v0.1 huggingface.co Url

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