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---
language:
- en
license: apache-2.0
library_name: transformers
datasets:
- NeuralNovel/Neural-Story-v1
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- text-generation
- conversational
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-gen
model-index:
- name: Mistral-7B-Instruct-v0.2-Neural-Story
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 64.08
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 83.97
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 60.67
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 66.89
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 75.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 38.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
      name: Open LLM Leaderboard
model_creator: NeuralNovel
model_name: Mistral-7B-Instruct-v0.2-Neural-Story
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
---
# NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story AWQ

- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
- Original model: [dolphin-2.8-experiment26-7b](https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b)

![Neural-Story](https://i.ibb.co/JFRYk6g/OIG-27.jpg)

## Model Summary

The **Mistral-7B-Instruct-v0.2-Neural-Story** model, developed by NeuralNovel and funded by Techmind, is a language model finetuned from Mistral-7B-Instruct-v0.2.

Designed to generate instructive and narrative text, with a specific focus on storytelling.
This fine-tune has been tailored to provide detailed and creative responses in the context of narrative and optimised for short story telling.

Based on mistralAI, with apache-2.0 license, suitable for commercial or non-commercial use.

[Join NeuralNovel Discord!](https://discord.gg/rJXGjmxqzS)

## 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-Instruct-v0.2-Neural-Story-AWQ"
system_message = "You are Mistral, 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
```