mamba-2.8b_dolly_instruction_polish

Finetuned state-space/mamba-3.8b using s3nh/polish_dolly instruction dataset.

pip install mamba_ssm

is needed to properly infer on this model. More detail explanation soon.

Axolotl config

base_model: state-spaces/mamba-2.8b
model_type: MambaLMHeadModel
tokenizer_type: AutoTokenizer
tokenizer_config: EleutherAI/gpt-neox-20b

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: s3nh/alpaca-dolly-instruction-only-polish
    type: alpaca
dataset_prepared_path:
val_set_size: 0.0
output_dir: ./mamba

sequence_len: 1024
sample_packing: false
pad_to_sequence_len: false

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-5

train_on_inputs: false
group_by_length: true

bf16: true
fp16: false
tf32: true
save_strategy: steps
gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint: true
local_rank:
logging_steps: 100
xformers_attention:
flash_attention:

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch:
save_steps: 3000
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
tokens:
save_safetensors: False