|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
language: |
|
- en |
|
tags: |
|
- conversational |
|
- math |
|
- distillation |
|
- mistral |
|
--- |
|
|
|
This is an experimental model. |
|
|
|
The idea is : |
|
- Calculate the difference in weights between a donor model(meta-math/MetaMath-Mistral-7B) and the base model(mistralai/Mistral-7B-v0.1). This difference represents how much each parameter needs to be adjusted to go from the base state to the donor state. |
|
|
|
``` |
|
vector = math_model.state_dict()[k] - base_model.state_dict()[k] |
|
``` |
|
|
|
- Vector retrieved from the result of step one, is added to third model(lex-hue/Delexa-7b). This should transfer **math** *skills* to our third model. |
|
|
|
``` |
|
vector = new_math_model.state_dict()[k] |
|
new_v = v + vector.to(v.device) |
|
v.copy_(new_v) |
|
``` |
|
|
|
### Example: |
|
|
|
``` |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import torch |
|
|
|
model_name = "aloobun/CosmicNoodle-7B" |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") |
|
|
|
|
|
prompt = "For the natural number A, the quotient of A divided by 9 is 6 and the remainder is 5. What is the value of A?\n" |
|
input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
|
tokens = model.generate(input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True) |
|
out = tokenizer.decode(tokens[0], skip_special_tokens=True) |
|
print(out) |
|
``` |
