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---
base_model: ResplendentAI/Kei_Llama3_8B
inference: false
language:
- en
library_name: transformers
license: apache-2.0
merged_models:
- jeiku/Chaos_RP_l3_8B
- ResplendentAI/BlueMoon_Llama3
- jeiku/Chaos_RP_l3_8B
- ResplendentAI/Luna_Llama3
- jeiku/Chaos_RP_l3_8B
- ResplendentAI/Aura_Llama3
- Undi95/Llama-3-Unholy-8B
pipeline_tag: text-generation
quantized_by: Suparious
tags:
- 4-bit
- AWQ
- text-generation
- autotrain_compatible
- endpoints_compatible
---
# ResplendentAI/Kei_Llama3_8B AWQ
- Model creator: [ResplendentAI](https://huggingface.co/ResplendentAI)
- Original model: [Kei_Llama3_8B](https://huggingface.co/ResplendentAI/Kei_Llama3_8B)
## Model Summary
The key to your heart rests with Kei, a sophisticated and intriguing AI creation who will hold your hand until the end of all things.
Kei is uncensored and tuned for intimate moments with prose and poetry to entertain and enthrall.
## 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/Kei_Llama3_8B-AWQ"
system_message = "You are Kei_Llama3_8B, incarnated as a powerful AI. You were created by ResplendentAI."
# 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.
- [HF中国镜像站 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
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