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--- |
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language: |
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- de |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- deutsch |
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- german |
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- seedbox |
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- mistral |
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datasets: |
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- seedboxai/multitask_german_examples_32k |
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pipeline_tag: text-generation |
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--- |
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# KafkaLM-7B-German-V0.1 |
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**KafkaLM 7b** is a Mistral 7b model - further pre-trained on a large German dataset from Björn Plüster and LAION. [leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) - which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German). |
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KafkaLM 7b is a [Seedbox](https://huggingface.co/seedboxai) project trained by [Dennis Dickmann](https://huggingface.co/doubledsbv). |
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**Why Kafka?** |
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The models are proficient, yet creative, and have some tendencies to linguistically push boundaries 😊 |
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## Model Details |
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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. |
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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. |
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### Dataset |
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I used a 8k filtered version of the following [seedboxai/multitask_german_examples_32k](https://huggingface.co/datasets/seedboxai/multitask_german_examples_32k) |
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### Prompt Format |
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This model follows the subsequent prompt format: |
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``` |
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<|system|> |
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Du bist ein freundlicher und hilfsbereiter KI-Assistent. Du beantwortest Fragen faktenorientiert und präzise, ohne dabei relevante Fakten auszulassen.</s> |
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<|user|> |
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Welche Möglichkeiten der energetischen Sanierung habe ich neben Solar und Energiespeicher?</s> |
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<|assistant|> |
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``` |
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### Inference |
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Getting started with the model is straightforward |
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```python |
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import transformers |
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model_id = "seedboxai/KafkaLM-7B-German-V0.1" |
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model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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tokenizer.padding_side = "right" |
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tokenizer.pad_token = tokenizer.unk_token |
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tokenizer.add_eos_token = False |
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def generate_prompt(input): |
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prompt = '' |
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sys_prompt = "Du bist ein freundlicher und hilfsbereiter KI-Assistent. Du beantwortest Fragen faktenorientiert und präzise, ohne dabei relevante Fakten auszulassen." |
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prompt += f"<|system|>\n{sys_prompt.strip()}</s>\n" |
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prompt += f"<|user|>\n{input.strip()}</s>\n" |
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prompt += f"<|assistant|>\n" |
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return prompt.strip() |
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def evaluate( |
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input, |
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temperature=0.7, |
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top_p=0.95, |
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top_k=50, |
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num_beams=3, |
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max_new_tokens=512, |
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#max_length=8192, |
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**kwargs, |
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): |
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prompt = generate_prompt(input) |
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#print(prompt) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].to(device) |
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attention_mask=inputs["attention_mask"].to(device) |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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no_repeat_ngram_size=3, |
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do_sample=True, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate( |
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early_stopping=False, |
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#eos_token_id=tokenizer.eos_token_id, |
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#pad_token_id=tokenizer.pad_token_id, |
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input_ids=input_ids, |
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attention_mask=attention_mask, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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max_new_tokens=max_new_tokens, |
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#max_length= max_length |
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) |
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s = generation_output.sequences[0] |
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output = tokenizer.decode(s) |
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return output #.split("<|assistant|>")[1].strip() |
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print(evaluate("Wer ist eigentlich dieser Kafka?")) |
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``` |
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## Disclaimer |
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The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. |
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This model should only be used for research purposes. The original Llama2 license and all restrictions of datasets used to train this model apply. |
