初始化项目，由ModelHub XC社区提供模型
Model: occiglot/occiglot-7b-fr-en-instruct Source: Original Platform
This commit is contained in:
ModelHub XC
137
README.md
Normal file
137
README.md
Normal file
@@ -0,0 +1,137 @@
|
||||
---
|
||||
license: apache-2.0
|
||||
language:
|
||||
- en
|
||||
- es
|
||||
pipeline_tag: text-generation
|
||||
---
|
||||
|
||||
|
||||
|
||||
# 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": "qui est le président français ?"},
|
||||
>>> ]
|
||||
>>> 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]):])
|
||||
'Le président français est Emmanuel Macron.'
|
||||
```
|
||||
|
||||
## 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>
|
||||
|
||||
|
||||
### English
|
||||
|
||||
| | arc_challenge | belebele | hellaswag | mmlu | truthfulqa | avg |
|
||||
|:-------------------------------------|----------------:|-----------:|------------:|---------:|-------------:|---------:|
|
||||
| occiglot/occiglot-7b-eu5 | 0.530717 | 0.726667 | 0.789882 | 0.531904 | 0.403678 | 0.59657 |
|
||||
| occiglot/occiglot-7b-eu5-instruct | 0.558874 | 0.746667 | 0.799841 | 0.535109 | 0.449034 | 0.617905 |
|
||||
| occiglot/occiglot-7b-de-en | 0.556314 | 0.791111 | 0.803824 | 0.568438 | 0.423251 | 0.628587 |
|
||||
| occiglot/occiglot-7b-de-en-instruct | 0.604096 | 0.812222 | 0.80004 | 0.570574 | 0.493807 | 0.656148 |
|
||||
| LeoLM/leo-mistral-hessianai-7b | 0.522184 | 0.736667 | 0.777833 | 0.538812 | 0.429248 | 0.600949 |
|
||||
| mistralai/Mistral-7B-v0.1 | 0.612628 | 0.844444 | 0.834097 | 0.624555 | 0.426201 | 0.668385 |
|
||||
| mistralai/Mistral-7B-Instruct-v0.2 | 0.637372 | 0.824444 | 0.846345 | 0.59201 | 0.668116 | 0.713657 |
|
||||
|
||||
### French
|
||||
|
||||
| | arc_challenge_fr | belebele_fr | hellaswag_fr | mmlu_fr | truthfulqa_fr | avg |
|
||||
|:-------------------------------------|-------------------:|--------------:|---------------:|----------:|----------------:|---------:|
|
||||
| occiglot/occiglot-7b-eu5 | 0.506416 | 0.675556 | 0.712358 | 0.495684 | 0.23507 | 0.525017 |
|
||||
| occiglot/occiglot-7b-eu5-instruct | 0.541488 | 0.7 | 0.724245 | 0.499122 | 0.306226 | 0.554216 |
|
||||
| occiglot/occiglot-7b-fr-en | 0.532934 | 0.706667 | 0.718891 | 0.51333 | 0.242694 | 0.542903 |
|
||||
| occiglot/occiglot-7b-fr-en-instruct | 0.542344 | 0.752222 | 0.72553 | 0.52051 | 0.29479 | 0.567079 |
|
||||
| OpenLLM-France/Claire-Mistral-7B-0.1 | 0.486741 | 0.694444 | 0.642964 | 0.479566 | 0.271919 | 0.515127 |
|
||||
| mistralai/Mistral-7B-v0.1 | 0.525235 | 0.776667 | 0.66481 | 0.543121 | 0.280813 | 0.558129 |
|
||||
| mistralai/Mistral-7B-Instruct-v0.2 | 0.551754 | 0.758889 | 0.67916 | 0.506837 | 0.382465 | 0.575821 |
|
||||
|
||||
</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
|
||||
- https://huggingface.co/NikolayKozloff/occiglot-7b-fr-en-GGUF
|
||||
Reference in New Issue
Block a user