Instructions to use ajibawa-2023/Young-Children-Storyteller-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/Young-Children-Storyteller-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/Young-Children-Storyteller-Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/Young-Children-Storyteller-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/Young-Children-Storyteller-Mistral-7B", device_map="auto")
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 Settings

vLLM

How to use ajibawa-2023/Young-Children-Storyteller-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/Young-Children-Storyteller-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/Young-Children-Storyteller-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ajibawa-2023/Young-Children-Storyteller-Mistral-7B

SGLang

How to use ajibawa-2023/Young-Children-Storyteller-Mistral-7B 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 "ajibawa-2023/Young-Children-Storyteller-Mistral-7B" \
    --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": "ajibawa-2023/Young-Children-Storyteller-Mistral-7B",
		"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 "ajibawa-2023/Young-Children-Storyteller-Mistral-7B" \
        --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": "ajibawa-2023/Young-Children-Storyteller-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ajibawa-2023/Young-Children-Storyteller-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/Young-Children-Storyteller-Mistral-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Young-Children-Storyteller-Mistral-7B

This model is based on my dataset Children-Stories-Collection which has over 0.9 million stories meant for Young Children (age 6 to 12).

Drawing upon synthetic datasets meticulously designed with the developmental needs of young children in mind, Young-Children-Storyteller is more than just a tool—it's a companion on the journey of discovery and learning. With its boundless storytelling capabilities, this model serves as a gateway to a universe brimming with wonder, adventure, and endless possibilities.

Whether it's embarking on a whimsical adventure with colorful characters, unraveling mysteries in far-off lands, or simply sharing moments of joy and laughter, Young-Children-Storyteller fosters a love for language and storytelling from the earliest of ages. Through interactive engagement and age-appropriate content, it nurtures creativity, empathy, and critical thinking skills, laying a foundation for lifelong learning and exploration.

Rooted in a vast repository of over 0.9 million specially curated stories tailored for young minds, Young-Children-Storyteller is poised to revolutionize the way children engage with language and storytelling.

Kindly note this is qLoRA version, another exception.

GGUF & Exllama

Standard Q_K & GGUF: Link

Exllama: TBA

Special Thanks to MarsupialAI for quantizing the model.

Training

Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took more than 30 Hours. Axolotl codebase was used for training purpose. Entire data is trained on Mistral-7B-v0.1.

Example Prompt:

This model uses ChatML prompt format.

<|im_start|>system
You are a Helpful Assistant who can write educational stories for Young Children.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

You can modify above Prompt as per your requirement.

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

Example 1

Example 2

Example 3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.08
AI2 Reasoning Challenge (25-Shot)	68.69
HellaSwag (10-Shot)	84.67
MMLU (5-Shot)	64.11
TruthfulQA (0-shot)	62.62
Winogrande (5-shot)	81.22
GSM8k (5-shot)	65.20

Downloads last month: 69

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ajibawa-2023/Young-Children-Storyteller-Mistral-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(947)

this model

Quantizations

1 model

Dataset used to train ajibawa-2023/Young-Children-Storyteller-Mistral-7B

Spaces using ajibawa-2023/Young-Children-Storyteller-Mistral-7B 14

Evaluation results

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

68.690
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.670
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.110
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

62.620
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.220
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

65.200