metadata
license: mit
Model Description
This model is a mixture of experts merge consisting of 3 mistral based models:
base model, openchat/openchat-3.5-0106
code expert, beowolx/CodeNinja-1.0-OpenChat-7B
math expert, meta-math/MetaMath-Mistral-7B
This is the config used in the merging process:
base_model: openchat/openchat-3.5-0106
experts:
- source_model: openchat/openchat-3.5-0106
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "I want"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- "C#"
- "C++"
- "debug"
- "runtime"
- "html"
- "command"
- "nodejs"
- source_model: meta-math/MetaMath-Mistral-7B
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
- "calculate"
- "arithmetic"
- "algebra"
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Chickaboo/Chicka-Mistral-4x7b")
tokenizer = AutoTokenizer.from_pretrained("Chickaboo/Chicka-Mistral-4x7b")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])