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

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story")
model = AutoModelForCausalLM.from_pretrained("NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story

SGLang

How to use NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story with Docker Model Runner:
```
docker model run hf.co/NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

GGUF FILES 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: 39

Safetensors

Model size

7B params

Tensor type

F16

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

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

(1088)

this model

Merges

5 models

Quantizations

3 models

Dataset used to train NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

64.080
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.970
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

60.670
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.890
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

75.850
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

38.290