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--- |
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base_model: mistralai/Mistral-Nemo-Base-2407 |
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library_name: peft |
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--- |
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# Model Card for Model ID |
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This is a [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) model fine-tuned on [Reddit-Dad-Jokes dataset](https://huggingface.co/datasets/shuttie/dadjokes). It can generate cringe and toxic jokes on any short prompt: |
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``` |
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[INST] What horse says when she sees an old lady? [/INST] Move over grandma, I'm going to trot! |
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[INST] What is a lawyer's favorite drink? [/INST] Whine. |
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[INST] My obese pet parrot died over the weekend. [/INST] Now I just have a big fat birdcage. |
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[INST] My wife asked why there was a blanket on top of me and my laptop. [/INST] I told her that I was just covering all my bases. |
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[INST] My girlfriend changed after she became a vegetarian. [/INST] She finally lost her meat. |
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``` |
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## Demo |
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The model is running on HF Spaces: [https://huggingface.co/spaces/shuttie/dadjokes](https://huggingface.co/spaces/shuttie/dadjokes) |
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## Used data |
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We use a [Kaggle Reddit Dad Jokes dataset](https://huggingface.co/datasets/shuttie/reddit-dadjokes) formatted in a base+punchline tuples. The model task was to predict the punchline given the base. Prompt format is the same as for original Mistral model: |
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`[INST] base [/INST] punchline` |
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## Training process |
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The model was trained with [Axolotl](TODO) with the following config: |
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```yaml |
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base_model: mistralai/Mistral-Nemo-Base-2407 |
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model_type: MistralForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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val_set_size: 0.01 |
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datasets: |
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- path: shuttie/reddit-dadjokes |
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split: train |
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type: |
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field_system: system |
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field_instruction: instruction |
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field_output: output |
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field_input: input |
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format: "[INST] {input} [/INST]" |
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dataset_prepared_path: last_run_prepared |
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output_dir: ./outputs/dadjoke-mistral-nemo-qlora-r128 |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 256 |
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sample_packing: false |
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pad_to_sequence_len: true |
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lora_r: 128 |
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lora_alpha: 64 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: "dad jokes" |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 16 |
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num_epochs: 1 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0001 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: false |
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gradient_checkpointing_kwargs: |
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use_reentrant: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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xformers_attention: |
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flash_attention: true |
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logging_steps: 10 |
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warmup_steps: 10 |
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evals_per_epoch: 10 |
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eval_table_size: |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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- full_shard |
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- auto_wrap |
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fsdp_config: |
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fsdp_limit_all_gathers: true |
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fsdp_sync_module_states: true |
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fsdp_offload_params: false |
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fsdp_use_orig_params: false |
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fsdp_cpu_ram_efficient_loading: false |
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fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer |
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fsdp_state_dict_type: FULL_STATE_DICT |
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
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activation_checkpointing: true |
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special_tokens: |
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pad_token: <pad> |
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flash_attention: true |
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``` |
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# License |
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Apache 2.0 |
