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SD-Prompter-1.5B-V0.1-GGUF
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---

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

---

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# QuantFactory/SD-Prompter-1.5B-V0.1-GGUF
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

# Original Model Card


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) 

## 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.