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---
license: mit
language:
- en
pipeline_tag: question-answering
---
# Llama-mt-lora
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned with LLaMA with 8 Nvidia A100-80G GPUs using 3,000,000 groups of conversations in the context of mathematics by students and facilitators on Algebra Nation (https://www.mathnation.com/). Llama-mt-lora consists of 32 layers and over 7 billion parameters, consuming up to 13.5 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively respond generation in a mathematical context.
### Here is how to use it with texts in HuggingFace
```python
import torch
import transformers
from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained("Fan21/Llama-mt-lora")
mdoel = LlamaForCausalLM.from_pretrained(
"Fan21/Llama-mt-lora",
load_in_8bit=False,
torch_dtype=torch.float16,
device_map="auto",
)
def generate_prompt(instruction, input=None):
if input:
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""
def evaluate(
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,
**kwargs,
):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Response:")[1].strip()
instruction = 'write your instruction here'
inputs = 'write your inputs here'
output= evaluate(instruction,
input=inputs,
temperature=0.1,#change the parameters by yourself
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,)
```
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