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---
tags:
- finetuned
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- instruct
- text-generation
- conversational
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- finetune
- chatml
model-index:
- name: samantha-1.1-westlake-7b
results: []
base_model: cognitivecomputations/samantha-1.1-westlake-7b
license: apache-2.0
datasets:
- cognitivecomputations/samantha-data
language:
- en
library_name: transformers
model_creator: Common Sense
model_name: Samantha-v1.1-WestLake-7B
model_type: mistral
pipeline_tag: text-generation
inference: false
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: Suparious
---
# Samantha 1.1 Westlake-7b AWQ
- Model creator: [Cognitive Computations](https://huggingface.co/cognitivecomputations)
- Original model: [Samantha 1.1 WestLake 7B](https://huggingface.co/cognitivecomputations/samantha-1.1-westlake-7b)
## Model Summary
Samantha-1.1-Westlake-7b is the Samantha-1.1 dataset trained on Westlake-7b model.
Unfortunately, while I trained her not to engage in sexual or romantic activities, she seems to have taken her own path. When prompted sweetly, she can be led astray.
I am not sure if this is because of the addition of system prompts, or because she was trained on WestLake base.
Anyway she's grown and makes her own decisions, I can't stop her now.
Be good to her.
## 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/samantha-1.1-westlake-7b-AWQ"
system_message = "You are Senzu, 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
```
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