README.md

---
base_model: l3utterfly/mistral-7b-v0.2-layla-v4
license: apache-2.0
tags:
  - finetuned
  - text-generation
  - autotrain_compatible
  - endpoints_compatible
  - chatml
  - quantized
  - 4-bit
  - AWQ
library_name: transformers
language:
  - en
model_creator: l3utterfly
model_name: mistral-7b-v0.2-layla-v4
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
---
# l3utterfly/mistral-7b-v0.2-layla-v4 AWQ

- Model creator: [l3utterfly](https://huggingface.co/l3utterfly)
- Original model: [mistral-7b-v0.2-layla-v4](https://huggingface.co/l3utterfly/mistral-7b-v0.2-layla-v4)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a500f3143b1c7b5807cec7/O0CsatpKzzfQApIOropH_.png)
(image by https://huggingface.co/Kronikus)

## Model Summary

Mistral 7B (v0.2) fine-tuned by the OpenHermes 2.5 dataset optimised for multi-turn conversation and character impersonation.

The dataset has been pre-processed by doing the following:
1. remove all refusals
2. remove any mention of AI assistant
3. split any multi-turn dialog generated in the dataset into multi-turn conversations records
4. added nfsw generated conversations from the Teatime dataset

- **Developed by:** l3utterfly
- **Funded by:** Layla Network
- **Model type:** Mistral
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Finetuned from model:** Mistral 7B (v0.2)

## 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/mistral-7b-v0.2-layla-v4-AWQ"
system_message = "You are Layla, 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
```