README.md

---
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)