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---
language:
- or
---
# Model Card for Model ID
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
## Model description
odiagenAI-model-v0 is based on Llama-7b and finetuned with 52k Odia translated data from the open-source Stanford-Alpaca, resulting in good Odia instruction understanding and response generation capabilities.
The code of Odia data generation and other detailed information can be found in our Github project repository: https://github.com/shantipriyap/OdiaGenAI.
This repo contains a low-rank adapter for LLaMA-7b fit on the Stanford Alpaca dataset.
## Training hyper-parameters
| Parameter | Value |
| ------ | ------ |
| Batch size | 128 |
| Learning rate | 3e-4 |
| Epochs | 2 |
|Cutoff length | 256 |
|Weight_decay | 0.001 |
|Warmup_rate | 0.1 |
|LR_scheduler | linear |
|Lora r | 16 |
|Lora target modules | (q_proj, k_proj, v_proj, o_proj) |
Model can be easily loaded with AutoModelForCausalLM.
``` python
import torch
from peft import PeftModel
import transformers
assert (
"LlamaTokenizer" in transformers._import_structure["models.llama"]
), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
BASE_MODEL = "decapoda-research/llama-7b-hf"
LORA_WEIGHTS = "OdiaGenAI/odiagenAI-model-v0"
model = LlamaForCausalLM.from_pretrained(
BASE_MODEL,
load_in_8bit=False,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
)
def generate_prompt(instruction, input=None):
if input:
return f"""ନିମ୍ନରେ ଏକ ନିର୍ଦ୍ଦେଶନାମା ଯାହାକି ଏକ କାର୍ଯ୍ୟକୁ ବର୍ଣ୍ଣନା କରେ, ଏକ ଇନପୁଟ୍ ସହିତ ଯୋଡି ଯାହା ପରବର୍ତ୍ତୀ ପ୍ରସଙ୍ଗ ପ୍ରଦାନ କରେ | ଏକ ପ୍ରତିକ୍ରିୟା ଲେଖନ୍ତୁ ଯାହା ଅନୁରୋଧକୁ ସଠିକ୍ ଭାବରେ ସମାପ୍ତ କରେ |
### ନିର୍ଦ୍ଦେଶ:
{instruction}
### ଇନପୁଟ୍:
{input}
### ପ୍ରତିକ୍ରିୟା:"""
else:
return f"""ନିମ୍ନରେ ଏକ ନିର୍ଦ୍ଦେଶ ଯାହାକି ଏକ କାର୍ଯ୍ୟକୁ ବର୍ଣ୍ଣନା କରେ | ଏକ ପ୍ରତିକ୍ରିୟା ଲେଖନ୍ତୁ ଯାହା ଅନୁରୋଧକୁ ସଠିକ୍ ଭାବରେ ସମାପ୍ତ କରେ |
### ନିର୍ଦ୍ଦେଶ:
{instruction}
### ପ୍ରତିକ୍ରିୟା:"""
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
**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=128,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print(output.split("### Response:")[1].strip())
```
Instructions for running it can be found at https://github.com/shantipriyap/OdiaGenAI.
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