README.md

---
license: llama2
base_model: LeoLM/leo-hessianai-7b
datasets:
- caretech-owl/wikiquote-de-quotes
language:
- de
pipeline_tag: text2text-generation
---


# Model Card for Model ID

This model is trained to generate german quotes for a given author. 
The full model can be tested at [spaces/caretech-owl/quote-generator-de](https://huggingface.co/spaces/caretech-owl/quote-generator-de),
here we provide a full model with a 8 bit quantization.



## Model Details

### Model Description

This fine-tuned model has been trained on the [caretech-owl/wikiquote-de-quotes](https://huggingface.co/datasets/caretech-owl/wikiquote-de-quotes) dataset. 
The model was trained on a prompt like this
```python

prompt_format = "<|im_start|>system\
Dies ist eine Unterhaltung zwischen einem\
intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt Antworten in Form von Zitaten.<|im_end|>\n\
<|im_start|>user\
Zitiere {author}<|im_end|>\n<\
|im_start|>assistant\n{quote}<|im_end|>\n"
```
Where author is itended to be provided by the user, the quote is of format ```quote + " - " + author```.
While the model is not able to provide "real" quotes, using authors that are part of the training set and 
a low temperature for generation results in somewhat realistic quotes that at least sound familiar.


- **Developed by:** [CareTech OWL](https://www.caretech-owl.de/)
- **Model type:** Causal decoder-only transformer language model
- **Language(s) (NLP):** German
- **License:** [llama2](https://github.com/facebookresearch/llama/blob/main/LICENSE)
- **Finetuned from model:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)

## Uses

```python
import torch
from ctransformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained(
    "caretech-owl/leo-hessionai-7B-quotes-gguf", model_type="llama")

system_prompt = """Dies ist eine Unterhaltung zwischen \
einem intelligenten, hilfsbereitem \
KI-Assistenten und einem Nutzer.
Der Assistent gibt Antworten in Form von Zitaten."""
prompt_format = "<|im_start|>system\n{system_prompt}\
<|im_end|>\n<|im_start|>user\nZitiere {prompt}\
<|im_end|>\n<|im_start|>assistant\n"

def get_quote(author:str, max_new_tokens:int=200):
    query = prompt_format.format(system_prompt=system_prompt, prompt= author)
    output = base_model(query, stop='<|im_end|>', max_new_tokens=max_new_tokens, temperature=0.2, top_k=10)
    print(output)

get_quote("Heinrich Heine")
```



## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 8
- tokenizer: None
- dataset: None
- group_size: 32
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- use_exllama: True
- max_input_length: None
- exllama_config: {'version': <ExllamaVersion.ONE: 1>}
- cache_block_outputs: True

### Framework versions


- PEFT 0.6.2