DarkArtsForge/Poe_v1
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How to use Naphula-Archives/MN-Raven-12B-v0c-Base-LoRA with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("Retreatcost/Mistral-Nemo-Base-2407-ChatML")
model = PeftModel.from_pretrained(base_model, "Naphula-Archives/MN-Raven-12B-v0c-Base-LoRA")axolotl version: 0.11.0.dev0
adapter: lora
base_model: Retreatcost/Mistral-Nemo-Base-2407-ChatML
bf16: true
datasets:
- ds_type: json
path: DarkArtsForge/Poe_v1
type: alpaca:chatml
flash_attention: true
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
hub_always_push: true
hub_model_id: Naphula-Archives/MN-Raven-12B-v0c-Base-LoRA
hub_private_repo: true
hub_strategy: checkpoint
is_mistral_derived_model: true
learning_rate: 5.0e-05
load_in_4bit: false
logging_steps: 1
lora_alpha: 128
lora_dropout: 0
lora_modules_to_save: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 1
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_torch_fused
output_dir: /runpod-volume/fine-tuning/default_run_id
pad_to_sequence_len: false
push_to_hub: true
resize_token_embeddings: false
run_name: default_run_id
runpod_job_id: 0
sample_packing: false
save_strategy: epoch
save_total_limit: 5
sequence_len: 512
special_tokens:
eos_token: <|im_end|>
pad_token: <pad>
tf32: true
tokenizer_type: AutoTokenizer
val_set_size: 0
This model is a fine-tuned version of Retreatcost/Mistral-Nemo-Base-2407-ChatML on the DarkArtsForge/Poe_v1 dataset.
{
"epoch": 3.0,
"grad_norm": 1.8156672716140747,
"learning_rate": 1.836022971459737e-05,
"loss": 0.0882,
"step": 300
},
More information needed
More information needed
The following hyperparameters were used during training:
Base model
mistralai/Mistral-Nemo-Base-2407