Create README.md

README.md

@@ -0,0 +1,113 @@
+---
+license: apache-2.0
+language:
+- en
+- es
+pipeline_tag: text-generation
+---
+
+![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)
+
+# Occiglot-7B-FR-EN-Instruct
+
+> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
+> 
+
+**Occiglot-7B-FR-EN-Instruct** is a the instruct version of [occiglot-7b-fr-en](https://huggingface.co/occiglot/occiglot-7b-fr-en), a generative language model with 7B parameters supporting the Spanish and English and trained by the [Occiglot Research Collective](https://occiglot.github.io/occiglot/).
+It was trained on 160M tokens of additional multilingual and code instructions.
+Note that the model was not safety aligned and might generate problematic outputs.
+
+This is the first release of an ongoing open research project for multilingual language models. 
+If you want to train a model for your own language or are working on evaluations, please contact us or join our [Discord server](https://discord.gg/wUpvYs4XvM). **We are open for collaborations!**
+
+
+### Model details
+
+- **Instruction tuned from:** [occiglot-7b-fr-en](https://huggingface.co/occiglot/occiglot-7b-fr-en)
+- **Model type:** Causal decoder-only transformer language model
+- **Languages:** English, Spanish, and code.
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
+- **Compute resources:** [DFKI cluster](https://www.dfki.de/en/web)
+- **Contributors:** Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
+- **Research labs:** [Occiglot](https://occiglot.github.io/occiglot/) with support from [SAINT](https://www.dfki.de/en/web/research/research-departments/foundations-of-systems-ai) and [SLT](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology)
+- **Contact:** [Discord](https://discord.gg/wUpvYs4XvM)
+
+### How to use
+
+The model was trained using the chatml instruction template. You can use the transformers chat template feature for interaction.
+Since the generation relies on some randomness, we
+set a seed for reproducibility:
+
+```python
+>>> from transformers import AutoTokenizer, MistralForCausalLM, set_seed
+>>> tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-es-en-instruct")
+>>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-es-en-instruct')  # You may want to use bfloat16 and/or move to GPU here
+>>> set_seed(42)
+>>> messages = [
+>>>    {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'},
+>>>    {"role": "user", "content": "¿quién es el presidente del gobierno español?"},
+>>> ]
+>>> tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=False, return_tensors='pt',)
+>>> set_seed(42)
+>>> outputs = model.generate(tokenized_chat.to('cuda'), max_new_tokens=200,)
+>>> tokenizer.decode(out[0][len(tokenized_chat[0]):])
+'Actualmente el presidente del gobierno español es Pedro Sánchez Pérez-Castejón'
+```
+
+## Dataset
+
+The training data was split evenly amongst French and English based on the total number of tokens.
+
+**English and Code**
+ - [Open-Hermes-2B](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
+
+
+**French**
+ - [Bactrian-X](https://huggingface.co/datasets/MBZUAI/Bactrian-X) (French subset)
+ - [AI-Society Translated](https://huggingface.co/datasets/camel-ai/ai_society_translated) (French subset)
+ - [GT-Dorimiti](https://huggingface.co/datasets/Gt-Doremiti/gt-doremiti-instructions)
+ - [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (French subset)
+ - [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (French subset)
+
+## Training settings
+
+- Full instruction fine-tuning on 8xH100.
+- 0.6 - 4 training epochs (depending on dataset sampling).
+- Framework: [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
+- Precision: bf16
+- Optimizer: AdamW
+- Global batch size: 128 (with 8192 context length)
+- Cosine Annealing with Warmup
+
+
+## Tokenizer
+
+Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
+
+## Evaluation
+
+Preliminary evaluation results can be found below. 
+Please note that the non-English results are based on partially machine-translated datasets and English prompts ([Belebele](https://huggingface.co/datasets/facebook/belebele) and [Okapi framework](https://github.com/nlp-uoregon/Okapi)) and thus should be interpreted with caution, e.g., biased towards English model performance.
+Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.
+
+<details>
+<summary>Evaluation results</summary>
+  
+
+</details>
+
+## Acknowledgements
+
+The pre-trained model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/) which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)).
+The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)
+through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
+
+
+## License
+
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
+
+## See also
+
+- https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01
+