Instructions to use s3nh/EduHelp-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s3nh/EduHelp-8B with Transformers:

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

pipe = pipeline("text-generation", model="s3nh/EduHelp-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("s3nh/EduHelp-8B")
model = AutoModelForCausalLM.from_pretrained("s3nh/EduHelp-8B")
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 s3nh/EduHelp-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s3nh/EduHelp-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s3nh/EduHelp-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/s3nh/EduHelp-8B

SGLang

How to use s3nh/EduHelp-8B 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 "s3nh/EduHelp-8B" \
    --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": "s3nh/EduHelp-8B",
		"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 "s3nh/EduHelp-8B" \
        --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": "s3nh/EduHelp-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use s3nh/EduHelp-8B with Docker Model Runner:
```
docker model run hf.co/s3nh/EduHelp-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EduHelper

EduHelper is a child-friendly tutoring assistant fine-tuned from the Qwen3-8B base model using parameter-efficient fine-tuning (PEFT) with LoRA on the ajibawa-2023/Education-Young-Children dataset.

TL;DR

Base model: Qwen3-8B
Method: PEFT (LoRA), adapters merged into the final weights
Training data: Education-Young-Children
Intended use: Gentle, age-appropriate explanations and basic tutoring for young learners
Language: Primarily English
Safety: Requires adult supervision; not a substitute for professional advice

Model Details

Architecture: Decoder-only LLM (chat/instruction style), based on Qwen3-8B
Training approach: Supervised fine-tuning with LoRA (via PEFT), adapters merged into the base model for standalone deployment
Focus: Clear, simple, supportive answers for early-learning contexts (e.g., basic reading, counting, everyday knowledge)

Please refer to the Qwen3-8B base model card for detailed architecture and licensing.

Intended Use and Limitations

Suitable for:
- Simple explanations and step-by-step guidance
- Basic arithmetic and counting practice
- Short reading comprehension and vocabulary support
- Everyday factual knowledge for children
Not suitable for:
- Medical, legal, or emergency advice
- Unsupervised use by children
- High-stakes or specialized professional tasks

The model can make mistakes or produce content that may not be perfectly age-appropriate. Always supervise and review outputs.

Training Data

Dataset: ajibawa-2023/Education-Young-Children
Description: Educational prompts and responses oriented toward young children
Notes: Review the dataset card for curation details and license. Ensure compliance when redistributing or deploying.

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
    {"role": "user", "content": "Can you explain what a verb is with two examples?"}
]

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Tips:

For more focused answers, try temperature=0.2–0.5.
Add a clear system prompt to reinforce gentle, age-appropriate behavior.

Safety and Responsible Use

Supervision: Children should use this model under adult supervision.
Content filtering: Consider additional filtering or guardrails to ensure age-appropriate outputs.
Biases: The model may reflect biases present in training data. Review outputs in your application context.