library_name: transformers
tags:
- deutsch
- german
- seedbox
- mistral
- mixtral
- multilingual
license: apache-2.0
language:
- de
- en
pipeline_tag: text-generation
KafkaLM-8x7b-German-V0.1
KafkaLM 8x7b is a MoE model based on Mistral AI´s Mixtral 8x7b which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German).
KafkaLM 8x7b is a Seedbox project trained by Dennis Dickmann.
Why Kafka? The models are proficient, yet creative, have some tendencies to linguistically push boundaries 😊
Model Details
The purpose of releasing the KafkaLM series is to contribute to the German AI community with a set of fine-tuned LLMs that are easy to use in everyday applications across a variety of tasks.
The main goal was to provide LLMs proficient in German, especially to be used in German-speaking business contexts where English alone is not sufficient.
DPO
The model has been aligned with a german and modified version of the ultra feedback dataset from huggingface.
Dataset
I used a 8k filtered version of the following seedboxai/multitask_german_examples_32k
Inference
Getting started with the model is straightforward
import transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "seedboxai/KafkaLM-Mixtral-8x7B-V0.2"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
pipeline = transformers.pipeline(
model=model, tokenizer=tokenizer,
return_full_text=True,
task='text-generation',
device="cuda",
)
messages = [
{"role": "system", "content": "Du bist ein hilfreicher KI-Assistent."},
{"role": "user", "content": "Wer ist eigentlich dieser Kafka?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("</s>")
]
outputs = pipeline(
prompt,
max_new_tokens=max_new_tokens,
eos_token_id=terminators,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model.