Instructions to use AdamLucek/EduMixtral-4x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdamLucek/EduMixtral-4x7B with Transformers:

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

pipe = pipeline("text-generation", model="AdamLucek/EduMixtral-4x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdamLucek/EduMixtral-4x7B")
model = AutoModelForCausalLM.from_pretrained("AdamLucek/EduMixtral-4x7B")
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 AdamLucek/EduMixtral-4x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdamLucek/EduMixtral-4x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/EduMixtral-4x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AdamLucek/EduMixtral-4x7B

SGLang

How to use AdamLucek/EduMixtral-4x7B 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 "AdamLucek/EduMixtral-4x7B" \
    --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": "AdamLucek/EduMixtral-4x7B",
		"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 "AdamLucek/EduMixtral-4x7B" \
        --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": "AdamLucek/EduMixtral-4x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdamLucek/EduMixtral-4x7B with Docker Model Runner:
```
docker model run hf.co/AdamLucek/EduMixtral-4x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EduMixtral-4x7B

EduMixtral-4x7B is an experimental model that combines different educational focused language models intended for downstream human/ai student/teacher application research. Intended to cover: general knowledge, medical field, math, and writing assistance.

🤏 Models Merged

EduMixtral-4x7B is a Mixture of Experts (MoE) made with the following models using Mergekit:

mlabonne/NeuralDaredevil-7B <- Base Model
BioMistral/BioMistral-7B
mistralai/Mathstral-7B-v0.1
FPHam/Writing_Partner_Mistral_7B

🧩 Configuration

base_model: mlabonne/NeuralDaredevil-7B
gate_mode: hidden
experts:
  - source_model: mlabonne/NeuralDaredevil-7B
    positive_prompts:
    - "hello"
    - "help"
    - "question"
    - "explain"
    - "information"
  - source_model: BioMistral/BioMistral-7B
    positive_prompts:
    - "medical"
    - "health"
    - "biomedical"
    - "clinical"
    - "anatomy"
  - source_model: mistralai/Mathstral-7B-v0.1
    positive_prompts:
    - "math"
    - "calculation"
    - "equation"
    - "geometry"
    - "algebra"
  - source_model: FPHam/Writing_Partner_Mistral_7B
    positive_prompts:
    - "writing"
    - "creative process"
    - "story structure"
    - "character development"
    - "plot"

💻 Usage

It is reccomended to load in 8bit or 4bit quantization

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("AdamLucek/EduMixtral-4x7B")
model = AutoModelForCausalLM.from_pretrained(
    "AdamLucek/EduMixtral-4x7B",
    device_map="cuda",
    quantization_config=BitsAndBytesConfig(load_in_8bit=True)
)

# Prepare the input text
input_text = "Math problem: Xiaoli reads a 240-page story book. She reads (1/8) of the whole book on the first day and (1/5) of the whole book on the second day. How many pages did she read in total in two days?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

# Generate the output with specified parameters
outputs = model.generate(
    **input_ids,
    max_new_tokens=256,
    num_return_sequences=1
)

# Decode and print the generated text
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Output:

Solution:
To find the total number of pages Xiaoli read in two days, we need to add the number of pages she read on the first day and the second day.
On the first day, Xiaoli read 1/8 of the book. Since the book has 240 pages, the number of pages she read on the first day is:
[ \frac{1}{8} \times 240 = 30 \text{ pages} ]
On the second day, Xiaoli read 1/5 of the book. The number of pages she read on the second day is:
[ \frac{1}{5} \times 240 = 48 \text{ pages} ]
To find the total number of pages she read in two days, we add the pages she read on the first day and the second day:
[ 30 \text{ pages} + 48 \text{ pages} = 78 \text{ pages} ]
Therefore, Xiaoli read a total of 78 pages in two days.
Final answer: Xiaoli read 78 pages in total