---
language:
- en
- sw
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---

# Uploaded  model

- **Developed by:** Mollel
- **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.




# Inference With Inference with HuggingFace transformers


```python3

!pip install transformers peft accelerate bitsandbytes

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained(
    "Mollel/Gemma_Swahili_Mollel_1_epoch",
    load_in_4bit = False
)

tokenizer = AutoTokenizer.from_pretrained("Mollel/Gemma_Swahili_Mollel_1_epoch")


input_prompt = """
### Instruction:
{}

### Input:
{}

### Response:
{}"""



input_text = input_prompt.format(
        "Andika aya fupi kuhusu mada iliyotolewa.", # instruction
        "Umuhimu wa kutumia nishati inayoweza kurejeshwa", # input
        "", # output - leave this blank for generation!
    )

inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
response = tokenizer.batch_decode(outputs)[0]

print(response)

```

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)