Mistral-7B-v0.3-sarcasm-scrolls-4k

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

axolotl version: 0.4.1

base_model: mistralai/Mistral-7B-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

strict: false

# dataset
datasets:
    - path: BEE-spoke-data/sarcasm-scrolls
      type: completion # format from earlier
      field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.025

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false

# WANDB
wandb_project: sarcasm-scrolls
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: Mistral-7B-v0.3-sarcasm-scrolls
hub_model_id: pszemraj/Mistral-7B-v0.3-sarcasm-scrolls-2
hub_strategy: every_save

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch_fused # paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5

load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: true

torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
logging_steps: 5
xformers_attention:
flash_attention: true

warmup_steps: 20
# hyperparams for freq of evals, saving, etc
evals_per_epoch: 4
saves_per_epoch: 4
save_safetensors: true
save_total_limit: 1 # Checkpoints saved at a time
output_dir: ./output-axolotl/output-model-theta
resume_from_checkpoint:


deepspeed:
weight_decay: 0.04

special_tokens:

</details><br>

Mistral-7B-v0.3-sarcasm-scrolls @ ctx 4k

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.2825

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 20
• num_epochs: 2

Training results

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1