uploaded readme

README.md

@@ -0,0 +1,197 @@
+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+karina - bnb 8bits
+- Model creator: https://huggingface.co/yodi/
+- Original model: https://huggingface.co/yodi/karina/
+
+
+
+
+Original model description:
+---
+datasets:
+- Local
+license: bigscience-bloom-rail-1.0
+language:
+- id
+pipeline_tag: text-generation
+---
+
+#  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)
+
+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'\n---\nAnswer:\n',result[0]['generated_text'])[1]
+
+    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
+
+