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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
- generated_from_trainer
model-index:
- name: Panda-7B-v0.1
results: []
license: apache-2.0
base_model: NeuralNovel/Panda-7B-v0.1
datasets:
- NeuralNovel/Creative-Logic-v1
- NeuralNovel/Neural-Story-v1
language:
- en
quantized_by: Suparious
pipeline_tag: text-generation
model_creator: NeuralNovel
model_name: Panda 7B 0.1
library_name: transformers
inference: false
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
---
# Panda 7B v0.1 AWQ
- Model creator: [NeuralNovel](https://huggingface.co/NeuralNovel)
- Original model: [Panda-7B-v0.1](https://huggingface.co/NeuralNovel/Panda-7B-v0.1)
## Model Details
The **Panda-7B-v0.1** model by NeuralNovel.
Fine-tuned with the intention to generate instructive and narrative text, with a specific focus on combining the elements of versatility, character engagement and nuanced writing capability.
This fine-tune has been designed to provide detailed, creative and logical responses in the context of diverse narratives. Optimised for creative writing, roleplay and logical problem solving.
Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2. Apache-2.0 license, suitable for commercial or non-commercial use.
*Sincere appreciation to Techmind for their generous sponsorship.*
## 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/Panda-7B-v0.1-DPO-AWQ"
system_message = "You are Panda, 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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