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--- |
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license: agpl-3.0 |
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tags: |
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- chat |
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datasets: |
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- NewEden/CivitAI-SD-Prompts |
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License: agpl-3.0 |
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Language: |
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- En |
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Pipeline_tag: text-generation |
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Base_model: NewEden/Qwen-1.5B-Claude |
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Tags: |
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- Chat |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/SD-Prompter-1.5B-V0.1-GGUF |
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This is quantized version of [Delta-Vector/SD-Prompter-1.5B-V0.1](https://huggingface.co/Delta-Vector/SD-Prompter-1.5B-V0.1) created using llama.cpp |
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# Original Model Card |
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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 |
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[NewEden/Qwen-1.5B-Claude](https://huggingface.co/NewEden/Qwen-1.5B-Claude). |
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## Prompting |
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Model has been tuned with the Alapaca formatting. A typical input would look like this: |
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``` |
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### Instruction: |
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Create a prompt for Stable Diffusion based on the information below. |
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### Input: |
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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. |
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### Response: |
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``` |
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## System Prompting |
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I would highly recommend using the following system prompt for this model. |
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``` |
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Create a prompt for Stable Diffusion based on the information below. |
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``` |
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## Axolotl Config |
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<details><summary>See Axolotl Trainer config</summary> |
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```yaml |
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base_model: NewEden/Qwen-1.5B-Claude |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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trust_remote_code: true |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: civit-slop-combined.jsonl |
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type: alpaca |
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conversation: mpt-30b-instruct |
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chat_template: alpaca |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./outputs/sd-prompter |
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sequence_len: 2048 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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adapter: |
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lora_model_dir: |
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lora_r: |
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lora_alpha: |
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lora_dropout: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: SDprompt-qwen |
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wandb_entity: |
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wandb_watch: |
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wandb_name: qwen1.5b-2 |
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wandb_log_model: |
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gradient_accumulation_steps: 64 |
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micro_batch_size: 2 |
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num_epochs: 3 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.00002 |
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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: true |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_ratio: 0.05 |
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evals_per_epoch: 4 |
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saves_per_epoch: 1 |
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debug: |
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#deepspeed: deepspeed_configs/zero2.json |
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#deepspeed: /training/axolotl/axolotl/deepspeed_configs/zero2.json |
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weight_decay: 0.0 |
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#fsdp: |
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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: true |
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# fsdp_use_orig_params: false |
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# fsdp_cpu_ram_efficient_loading: true |
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# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
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# fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer |
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# fsdp_state_dict_type: FULL_STATE_DICT |
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special_tokens: |
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``` |
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</details><br> |
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## Credits |
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Thank you to [Kubernetes Bad](https://huggingface.co/kubernetes-bad) |
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## Training |
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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. |
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