README.md

---
datasets:
- Local
license: bigscience-bloom-rail-1.0
language:
- id
pipeline_tag: text-generation
duplicated_from: yodi/karina
---

#  Table of Contents

1. [Model Summary](#model-summary)
2. [Use](#use)
4. [Training](#training)

# Model Summary

> We present KARINA, finetuned from BLOOMZ bigscience/bloomz-3b, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOMZ pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.

# Use

## Intended use

We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"*", the model will most likely answer "*Saya Karina. Ada yang bisa saya bantu?*".

## How to use

### CPU

<details>
<summary> Click to expand </summary>

```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "yodi/karina"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

inputs = tokenizer.encode("Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

</details>

### GPU in 4 bit

<details>
<summary> Click to expand </summary>

```python
# pip install -q transformers 
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline

MODEL_NAME = "yodi/karina"

model_4bit = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda:1", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"

generator = pipeline('text-generation',
                     model=model_4bit,
                     tokenizer=tokenizer,
                     do_sample=False)

result = generator(prompt, max_length=256)
print(result)

```

</details>

### GPU in 8bit

<details>
<summary> Click to expand </summary>

```python
# pip install -q transformers 
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline

MODEL_NAME = "yodi/karina"

model_4bit = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda:1", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"

generator = pipeline('text-generation',
                     model=model_4bit,
                     tokenizer=tokenizer,
                     do_sample=False)

result = generator(prompt, max_length=256)
print(result)
```

</details>

```
[{'generated_text': 'Given the question:\n{ siapa kamu? }\n---\nAnswer:\nSaya Karina, asisten virtual siap membantu seputar estimasi harga atau pertanyaan lain'}]
```

### Infer in Local with Gradio 

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
import re

import gradio as gr

MODEL_NAME = "yodi/karina"

model_4bit = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda:1", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"

generator = pipeline('text-generation',
                     model=model_4bit,
                     tokenizer=tokenizer,
                     do_sample=False)

def preprocess(text):
    return f"Given the question:\n{{ {text} }}\n---\nAnswer:\n"

def generate(text):
    preprocess_result = preprocess(text)
    result = generator(preprocess_result, max_length=256)
    output = re.split(r'\Given the question:|Answer:|Answer #|Title:',result[0]['generated_text'])[2]

    return output

with gr.Blocks() as demo:
    input_text = gr.Textbox(label="Input", lines=1)
    button = gr.Button("Submit")
    output_text = gr.Textbox(lines=6, label="Output")
    button.click(generate, inputs=[input_text], outputs=output_text)

demo.launch(enable_queue=True, debug=True)
```
And open the gradio url from browser.

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16

### Framework versions

- PEFT 0.5.0.dev0

<!-- Necessary for whitespace -->
###

# Limitations

**Prompt Engineering:** The performance may vary depending on the prompt and its following BLOOMZ models.

# Training

## Model

- **Architecture:** Same as [bloom](https://huggingface.co/bigscience/bloom), also refer to the `config.json` file