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OdiaGenAI-LLM/Llama3_8B_Odia_Unsloth
Llama3_8B_Odia_Unsloth is a machine learning model from OdiaGenAI-LLM. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as llama3.
Llama38BOdiaUnsloth is a fine-tuned Odia large language model with 8 billion parameters, and it is based on Llama3. The model is fine-tuned on a comprehensive 171k Odia instruction set, encompassing domain-specific an…
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Updated Apr 28, 2024
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From the Hugging Face model README
Llama3_8B_Odia_Unsloth is a fine-tuned Odia large language model with 8 billion parameters, and it is based on Llama3. The model is fine-tuned on a comprehensive 171k Odia instruction set, encompassing domain-specific and cultural nuances.
The fine-tuning process leverages Unsloth, expediting the training process for optimal efficiency.
For more details about the model, data, training procedure, and evaluations, go through the blog post.
Sample inference script.
#Install Unsloth
%%capture
import torch
major_version, minor_version = torch.cuda.get_device_capability()
# Must install separately since Colab has torch 2.2.1, which breaks packages
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
if major_version >= 8:
# Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)
!pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
else:
# Use this for older GPUs (V100, Tesla T4, RTX 20xx)
!pip install --no-deps xformers trl peft accelerate bitsandbytes
pass
from unsloth import FastLanguageModel
import torch
max_seq_length = 2048
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "OdiaGenAI-LLM/Llama3_8B_Odia_Unsloth",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
alpaca_prompt = """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:
{}
### Input:
{}
### Response:
{}"""
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
alpaca_prompt.format(
"କୋଭିଡ୍ 19 ର ଲକ୍ଷଣଗୁଡ଼ିକ କ’ଣ?", # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True)
tokenizer.batch_decode(outputs)
If you find this model useful, please consider giving 👏 and citing:
@misc{Llama3_8B_Odia_Unsloth,
author = {Shantipriya Parida and Sambit Sekhar and Debasish Dhal and Shakshi Panwar},
title = {OdiaGenAI Releases Llama3 Fine-tuned Model for the Odia Language},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}