---
base_model: jeiku/Mewthree_7B
inference: false
language:
- en
library_name: transformers
license: other
merged_models:
- Krisbiantoro/mistral7b_dpo_en
model_creator: jeiku
model_name: Mewthree-7B
model_type: mistral
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:
- mergekit
- merge
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- text-generation
- conversational
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- chatml
---
# jeiku/Mewthree-7B AWQ

- Model creator: [jeiku](https://huggingface.co/jeiku)
- Original model: [Mewthree-7B](https://huggingface.co/jeiku/Mewthree_7B)

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/mfFtubKCh143741_enqN8.jpeg)

## Model Summary

Draws upon the Prodigy lineage with some no robots tossed in for good measure. Dipped its toes in some memerboard essence and added a kiss of BioMistral for anatomy. Applied a DPO LoRA over top.

Seems to do markdown well. It's an overall balanced model with a focus on RP.

## 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/Mewthree-7B-AWQ"
system_message = "You are Dolphin, a helpful AI assistant."

# 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
```