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README.md

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+---
+language:
+- en
+- it
+license: apache-2.0
+tags:
+- exl2
+pipeline_tag: text-generation
+---
+
+![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)
+
+# Occiglot-7B-it-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-EU5-Instruct** is a the instruct version of [occiglot-7b-it-en](https://huggingface.co/occiglot/occiglot-7b-it-en), a generative language model with 7B parameters supporting the top-5 EU languages (English, Spanish, French, German, and Italian) 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-it-en](https://huggingface.co/occiglot/occiglot-7b-it-en)
+- **Model type:** Causal decoder-only transformer language model
+- **Languages:** English, Italian, 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-eu5-instruct")
+>>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-eu5-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": "chi è il primo ministro italiano?"},
+>>> ]
+>>> 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]):])
+'Il primo ministro italiano è attualmente Giorgia Meloni, presidente di Fratelli d'Italia, un partito politico di estrema destra.'
+```
+
+## Dataset
+
+The training data was split evenly amongst the 5 languages based on the total number of tokens. We would like to thank Disco Research and Björn Plüster for making their dataset available to us. 
+
+**English and Code**
+ - [Open-Hermes-2B](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
+
+**Italian**
+ - [Quora-IT-Baize](https://huggingface.co/datasets/andreabac3/Quora-Italian-Fauno-Baize)
+ - [Stackoverflow-IT-Vaize](https://huggingface.co/datasets/andreabac3/StackOverflow-Italian-Fauno-Baize)
+ - [Camoscio](https://huggingface.co/datasets/teelinsan/camoscio_cleaned)
+ - [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (Italian subset)
+ - [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (Italian 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-it-en           |        0.580205 |   0.774444 |    0.804222 | 0.578977 |     0.412786 | 0.630127 |
+| occiglot/occiglot-7b-it-en-instruct  |        0.609215 |   0.82     |    0.809301 | 0.578835 |     0.479562 | 0.659383 |
+| galatolo/cerbero-7b                  |        0.613481 |   0.827778 |    0.810396 | 0.600484 |     0.480911 | 0.66661  |
+| 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 |
+
+
+### Italian
+
+|                                      |   arc_challenge_it |   belebele_it |   hellaswag_it |   mmlu_it |   truthfulqa_it |      avg |
+|:-------------------------------------|-------------------:|--------------:|---------------:|----------:|----------------:|---------:|
+| occiglot/occiglot-7b-eu5             |           0.501283 |      0.652222 |       0.700533 |         0 |        0.252874 | 0.421382 |
+| occiglot/occiglot-7b-eu5-instruct    |           0.516681 |      0.661111 |       0.71326  |         0 |        0.295019 | 0.437214 |
+| occiglot/occiglot-7b-it-en           |           0.536356 |      0.684444 |       0.694768 |         0 |        0.247765 | 0.432667 |
+| occiglot/occiglot-7b-it-en-instruct  |           0.545766 |      0.717778 |       0.713804 |         0 |        0.303959 | 0.456261 |
+| galatolo/cerbero-7b                  |           0.522669 |      0.717778 |       0.631567 |         0 |        0.302682 | 0.434939 |
+| mistralai/Mistral-7B-v0.1            |           0.502139 |      0.734444 |       0.630371 |         0 |        0.264368 | 0.426264 |
+| mistralai/Mistral-7B-Instruct-v0.2   |           0.519247 |      0.703333 |       0.6394   |         0 |        0.349936 | 0.442383 |
+
+
+</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-it-en-GGUF

added_tokens.json

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+{
+  "<|im_end|>": 32001,
+  "<|im_start|>": 32000
+}

config.json

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+{
+    "_name_or_path": "occiglot/occiglot-7b-it-en-instruct",
+    "architectures": [
+        "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 10000.0,
+    "sliding_window": 4096,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.40.0.dev0",
+    "use_cache": false,
+    "vocab_size": 32002,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.17",
+        "bits": 5.0,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "wikitext-test.parquet"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.0.dev0"
+}

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