uf-aice-lab
commited on
Commit
•
1ea118a
1
Parent(s):
1fe67bf
Rename README (1).md to README.md
Browse files
README (1).md → README.md
RENAMED
@@ -4,19 +4,19 @@ language:
|
|
4 |
- en
|
5 |
pipeline_tag: question-answering
|
6 |
---
|
7 |
-
# Llama-
|
8 |
|
9 |
<!-- Provide a quick summary of what the model is/does. -->
|
10 |
|
11 |
-
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.
|
12 |
### Here is how to use it with texts in HuggingFace
|
13 |
```python
|
14 |
import torch
|
15 |
import transformers
|
16 |
from transformers import LlamaTokenizer, LlamaForCausalLM
|
17 |
-
tokenizer = LlamaTokenizer.from_pretrained("
|
18 |
mdoel = LlamaForCausalLM.from_pretrained(
|
19 |
-
"
|
20 |
load_in_8bit=False,
|
21 |
torch_dtype=torch.float16,
|
22 |
device_map="auto",
|
|
|
4 |
- en
|
5 |
pipeline_tag: question-answering
|
6 |
---
|
7 |
+
# Llama-2-Qlora
|
8 |
|
9 |
<!-- Provide a quick summary of what the model is/does. -->
|
10 |
|
11 |
+
This model is fine-tuned with LLaMA-2 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.
|
12 |
### Here is how to use it with texts in HuggingFace
|
13 |
```python
|
14 |
import torch
|
15 |
import transformers
|
16 |
from transformers import LlamaTokenizer, LlamaForCausalLM
|
17 |
+
tokenizer = LlamaTokenizer.from_pretrained("uf-aice-lab/Llama-2-QLoRA")
|
18 |
mdoel = LlamaForCausalLM.from_pretrained(
|
19 |
+
"uf-aice-lab/Llama-2-QLoRA",
|
20 |
load_in_8bit=False,
|
21 |
torch_dtype=torch.float16,
|
22 |
device_map="auto",
|