license: cc-by-nc-4.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
base_model:
- mlabonne/NeuralDaredevil-7B
- BioMistral/BioMistral-7B
- mistralai/Mathstral-7B-v0.1
- FPHam/Writing_Partner_Mistral_7B
library_name: transformers
pipeline_tag: text-generation
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