Instructions to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF", dtype="auto")

llama-cpp-python

How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF",
	filename="ggml-model-q2_k.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with Ollama:
```
ollama run hf.co/Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M
```

Unsloth Studio

How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF to start chatting

Docker Model Runner
How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with Docker Model Runner:
```
docker model run hf.co/Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M
```

Lemonade

How to use Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.2-Neural-Story-GGUF-Q4_K_M

List all available models

lemonade list

NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story

BASE MODEL HERE

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.

Data-set

The model was finetuned using the Neural-Story-v1 dataset.

Benchmark

Metric	Value
Avg.	64.96
ARC	64.08
HellaSwag	66.89
MMLU	60.67
TruthfulQA	66.89
Winogrande	75.85
GSM8K	38.29

Evaluated on HuggingFaceH4/open_llm_leaderboard

Summary

Fine-tuned with the intention of generating creative and narrative text, making it more suitable for creative writing prompts and storytelling.

Out-of-Scope Use

The model may not perform well in scenarios unrelated to instructive and narrative text generation. Misuse or applications outside its designed scope may result in suboptimal outcomes.

Bias, Risks, and Limitations

The model may exhibit biases or limitations inherent in the training data. It is essential to consider these factors when deploying the model to avoid unintended consequences.

While the Neural-Story-v0.1 dataset serves as an excellent starting point for testing language models, users are advised to exercise caution, as there might be some inherent genre or writing bias.

Hardware and Training


  n_epochs = 3,
  n_checkpoints = 3,
  batch_size = 12,
  learning_rate = 1e-5,

Sincere appreciation to Techmind for their generous sponsorship.

Downloads last month: 122

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for Kquant03/Mistral-7B-Instruct-v0.2-Neural-Story-GGUF

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Mistral-7B-Instruct-v0.2-Neural-Story-GGUF