| | --- |
| | license: agpl-3.0 |
| | tags: |
| | - chat |
| | datasets: |
| | - NewEden/CivitAI-SD-Prompts |
| | License: agpl-3.0 |
| | Language: |
| | - En |
| | Pipeline_tag: text-generation |
| | Base_model: NewEden/Qwen-1.5B-Claude |
| | Tags: |
| | - Chat |
| | --- |
| | |
| | This is the first in a line of models dedicated to creating Stable-Diffusion prompts when given a character appearance, This has been finetuned ontop of |
| | [NewEden/Qwen-1.5B-Claude](https://huggingface.co/NewEden/Qwen-1.5B-Claude). |
| |
|
| | ## Prompting |
| |
|
| | Model has been tuned with the Alapaca formatting. A typical input would look like this: |
| | ``` |
| | ### Instruction: |
| | Create a prompt for Stable Diffusion based on the information below. |
| | ### Input: |
| | Rae has short has dark brown hair and brown eyes, She is commonly seen wearing her Royal Academy uniform, which consists of a red jacket with gold lines, a white ruffled necktie, a red bow tie with an attached blue gem, and a long black skirt with white lines. Along with her uniform, she wears black leggings and brown shoes. |
| | ### Response: |
| | ``` |
| |
|
| | ## System Prompting |
| |
|
| | I would highly recommend using the following system prompt for this model. |
| |
|
| | ``` |
| | Create a prompt for Stable Diffusion based on the information below. |
| | ``` |
| |
|
| | ## Axolotl Config |
| |
|
| | <details><summary>See Axolotl Trainer config</summary> |
| |
|
| | ```yaml |
| | base_model: NewEden/Qwen-1.5B-Claude |
| | model_type: AutoModelForCausalLM |
| | tokenizer_type: AutoTokenizer |
| | |
| | trust_remote_code: true |
| | |
| | load_in_8bit: false |
| | load_in_4bit: false |
| | strict: false |
| | |
| | datasets: |
| | - path: civit-slop-combined.jsonl |
| | type: alpaca |
| | conversation: mpt-30b-instruct |
| | |
| | chat_template: alpaca |
| | |
| | dataset_prepared_path: |
| | val_set_size: 0.05 |
| | output_dir: ./outputs/sd-prompter |
| | sequence_len: 2048 |
| | sample_packing: true |
| | eval_sample_packing: false |
| | pad_to_sequence_len: true |
| | |
| | adapter: |
| | lora_model_dir: |
| | lora_r: |
| | lora_alpha: |
| | lora_dropout: |
| | lora_target_linear: true |
| | lora_fan_in_fan_out: |
| | |
| | wandb_project: SDprompt-qwen |
| | wandb_entity: |
| | wandb_watch: |
| | wandb_name: qwen1.5b-2 |
| | wandb_log_model: |
| | |
| | gradient_accumulation_steps: 64 |
| | micro_batch_size: 2 |
| | num_epochs: 3 |
| | optimizer: adamw_torch |
| | lr_scheduler: cosine |
| | learning_rate: 0.00002 |
| | |
| | train_on_inputs: false |
| | group_by_length: false |
| | bf16: auto |
| | fp16: |
| | tf32: true |
| | |
| | gradient_checkpointing: true |
| | gradient_checkpointing_kwargs: |
| | use_reentrant: false |
| | early_stopping_patience: |
| | resume_from_checkpoint: |
| | local_rank: |
| | logging_steps: 1 |
| | xformers_attention: |
| | flash_attention: true |
| | |
| | warmup_ratio: 0.05 |
| | evals_per_epoch: 4 |
| | saves_per_epoch: 1 |
| | debug: |
| | #deepspeed: deepspeed_configs/zero2.json |
| | #deepspeed: /training/axolotl/axolotl/deepspeed_configs/zero2.json |
| | weight_decay: 0.0 |
| | #fsdp: |
| | #fsdp_config: |
| | # fsdp_limit_all_gathers: true |
| | # fsdp_sync_module_states: true |
| | # fsdp_offload_params: true |
| | # fsdp_use_orig_params: false |
| | # fsdp_cpu_ram_efficient_loading: true |
| | # fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
| | # fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer |
| | # fsdp_state_dict_type: FULL_STATE_DICT |
| | special_tokens: |
| | ``` |
| | </details><br> |
| |
|
| | ## Credits |
| |
|
| | Thank you to [Kubernetes Bad](https://huggingface.co/kubernetes-bad), [Lucy Knada](https://huggingface.co/lucyknada), [CelineDion](https://huggingface.co/CelineDion), [Intervitens](https://huggingface.co/intervitens), [Kalomaze](https://huggingface.co/kalomaze) and the rest of [Anthracite](https://huggingface.co/anthracite-org) (But not Alpin.) |
| |
|
| | ## Training |
| | The training was done for 2 epochs. I used 2 x [RTX 6000s](https://www.nvidia.com/en-us/design-visualization/rtx-6000/) GPUs graciously provided by [Kubernetes Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model. |
