metadata
license: llama3
language:
- ne
library_name: transformers
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- unsloth
- pytorch
- llama-3
- conversational
This model is the initial test version, finetuned using LLaMA-3-8B version provided by UnslothAI in Nepali Language.
Model Details
Directly quantized 4bit model with bitsandbytes. Built with Meta Llama 3. By UnslothAI.
- Developed by: Norden Ghising Tamang under DarviLab Pvt. Ltd
- Model type: Transformer-based language model
- Language(s) (NLP): Nepali
- License: A custom commercial license is available at: https://llama.meta.com/llama3/license
How To Use
Using HuggingFace's AutoModelForPeftCausalLM
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"nordenxgt/nelm-chat-unsloth-llama3-v.0.0.1"
load_in_4bit=True
)
tokenizer = AutoTokenizer.from_pretrained("nordenxgt/nelm-chat-unsloth-llama3-v.0.0.1")
Using UnslothAI [x2 Faster Inference]
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="nordenxgt/nelm-chat-unsloth-llama3-v.0.0.1",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
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:
{}"""
inputs = tokenizer(
[
alpaca_prompt.format(
"गौतम बुद्धको जन्म कुन देशमा भएको थियो?" # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
tokenizer.batch_decode(outputs)