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---
license: apache-2.0
language:
- hi
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
pipeline_tag: text-generation
---
# Uploaded model
- **Developed by:** Ellight
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# Hindi-Gemma-2B-instruct (Instruction-tuned)
Hindi-Gemma-2B-instruct is an instruction-tuned Hindi large language model (LLM) with 2 billion parameters, and it is based on Gemma 2B.
# TO do inference using the LORA adapters
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "Ellight/gemma-2b-bnb-4bit", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
prompt = """
### Instruction:
{}
### Response:
{}"""
inputs = tokenizer(
[
prompt.format(
"शतरंज बोर्ड पर कितने वर्ग होते हैं?", # instruction
"", # 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) |