rainbowfish-v7-lora-adapter

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: andysalerno/mistral-sft-v3
model_type: AutoModelForCausalLM

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: andysalerno/rainbowfish-v1
    type:
      system_prompt: ""
      field_system: system
      field_instruction: input
      field_output: output
      format: "{instruction}"
      no_input_format: "{instruction}"
dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./lora-out-rainbow7

adapter: lora
lora_model_dir:

sequence_len: 2048
sample_packing: false # was true
eval_sample_packing: false
pad_to_sequence_len: false
padding_side: left

lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

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

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
# early_stopping_patience: 3
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

hub_strategy: "every_save"
hub_model_id: andysalerno/rainbowfish-v7

num_epochs: 2
warmup_steps: 100 
# warmup_ratio: 0.1
eval_steps: 200
eval_table_size:
eval_table_max_new_tokens: 128
# save_steps: 5
# max_steps: 400
saves_per_epoch: 2
debug:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  unk_token: "<unk>"

</details><br>

rainbowfish-v7

This model is a fine-tuned version of andysalerno/mistral-sft-v3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6464

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 2

Training results

Framework versions

• PEFT 0.8.2
• Transformers 4.38.0.dev0
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0