instruct-igel-001
Maintainer: philschmid
47
👁️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The instruct-igel-001 model is an instruction-tuned German large language model (LLM) developed by philschmid on top of the BigScience BLOOM model, which was adapted to German by Malte Ostendorff. The goal of this test was to explore the potential of the BLOOM architecture for language modeling tasks that require instruction-based responses.
The model was fine-tuned using a dataset of naive translations of instruction-based content from English to German. While this approach may introduce errors in the translated content, the model was able to learn to generate instruction-based responses in German. However, the model also exhibits common deficiencies of language models, including hallucination, toxicity, and stereotypes.
Similar German-focused language models include the EM German model family, which is based on the LeoLM Mistral architecture and offers versions in Llama2 7b, 13b and 70b as well as Mistral and LeoLM models. The DiscoLM German 7b v1 model is another Mistral-based German LLM focused on everyday use.
Model inputs and outputs
Inputs
- The
instruct-igel-001model takes in natural language text as input, similar to other large language models.
Outputs
- The model generates natural language text as output, with a focus on instruction-based responses.
Capabilities
The instruct-igel-001 model is designed to provide accurate and reliable language understanding capabilities for a wide range of natural language understanding tasks, including sentiment analysis, language translation, and question answering. While the model exhibits some common deficiencies, it can be a useful tool for German language applications that require instruction-based responses.
What can I use it for?
The instruct-igel-001 model could be used for a variety of German language applications, such as:
- Automated assistants and chatbots that need to provide instruction-based responses
- Sentiment analysis and text classification for German language content
- Language translation between German and other languages
The model could also be fine-tuned further on specific datasets or tasks to improve its performance.
Things to try
One interesting thing to try with the instruct-igel-001 model is to explore its capabilities and limitations around instruction-based responses. You could provide the model with a variety of German language instructions and observe how it responds, paying attention to any hallucinations, biases, or other issues that arise. This could help inform the development of future instruction-tuned German language models.
Additionally, you could experiment with using the model for tasks like sentiment analysis or language translation, and compare its performance to other German language models to understand its strengths and weaknesses.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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