--- base_model: ResplendentAI/DaturaCookie_7B datasets: - ResplendentAI/Luna_NSFW_Text - unalignment/toxic-dpo-v0.2 - ResplendentAI/Synthetic_Soul_1k - grimulkan/theory-of-mind - lemonilia/LimaRP - PygmalionAI/PIPPA inference: false language: - en library_name: transformers license: other merged_models: - ResplendentAI/Datura_7B - ChaoticNeutrals/Cookie_7B pipeline_tag: text-generation prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: Suparious tags: - mistral - 4-bit - AWQ - text-generation - autotrain_compatible - endpoints_compatible - chatml - not-for-all-audiences --- # ResplendentAI/DaturaCookie_7B AWQ - Model creator: [ResplendentAI](https://huggingface.co/ResplendentAI) - Original model: [DaturaCookie_7B](https://huggingface.co/ResplendentAI/DaturaCookie_7B) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/5jG2dft51fgPcGUGc-4Ym.png) ## Model Summary Proficient at roleplaying and lightehearted conversation, this model is prone to NSFW outputs. # Vision/multimodal capabilities: If you want to use vision functionality: You must use the latest versions of Koboldcpp. To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo. You can load the mmproj by using the corresponding section in the interface: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/UxH8OteeRbD1av1re0yNZ.png) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/DaturaCookie_7B-AWQ" system_message = "You are DaturaCookie, incarnated as a powerful AI." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code ## Prompt template: ChatML ```plaintext <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ```