johannhartmann
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Upload folder using huggingface_hub
Browse files- README.md +150 -0
- added_tokens.json +4 -0
- config.json +26 -0
- qmodel.pt +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +59 -0
README.md
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---
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language:
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- en
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- de
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license: apache-2.0
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tags:
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- hqq
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pipeline_tag: text-generation
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---
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![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)
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# Occiglot-7B-DE-EN-Instruct bf16 format
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> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
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>
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**Occiglot-7B-DE-EN-Instruct** is a the instruct version of [occiglot-7b-eu5](https://huggingface.co/occiglot/occiglot-7b-eu5/), a generative language model with 7B parameters supporting German and English and trained by the [Occiglot Research Collective](https://occiglot.github.io/occiglot/).
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It was trained on 180M tokens of additional multilingual and code instructions.
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Note that the model was not safety aligned and might generate problematic outputs.
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This is the first release of an ongoing open research project for multilingual language models.
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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!**
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*Special thanks go to **[Disco Research](https://huggingface.co/DiscoResearch)**, **[Jan Philipp Harries](https://huggingface.co/jphme)**, and **[Björn Plüster](https://huggingface.co/bjoernp)** for sharing the German dataset with us*
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### Model details
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- **Instruction tuned from:** [occiglot-7b-de-en](https://huggingface.co/occiglot/occiglot-7b-de-en)
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- **Model type:** Causal decoder-only transformer language model
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- **Languages:** English, German, and code.
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
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- **Compute resources:** [DFKI cluster](https://www.dfki.de/en/web)
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- **Contributors:** Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
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- **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)
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- **Contact:** [Discord](https://discord.gg/wUpvYs4XvM)
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### How to use
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The model was trained using the chatml instruction template. You can use the transformers chat template feature for interaction.
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Since the generation relies on some randomness, we
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set a seed for reproducibility:
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```python
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>>> from transformers import AutoTokenizer, MistralForCausalLM, set_seed
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>>> tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-de-en-instruct")
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>>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-de-en-instruct') # You may want to use bfloat16 and/or move to GPU here
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>>> set_seed(42)
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>>> messages = [
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>>> {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'},
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>>> {"role": "user", "content": "Wer ist der deutsche Bundeskanzler?"},
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>>> ]
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>>> tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=False, return_tensors='pt',)
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>>> set_seed(42)
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>>> outputs = model.generate(tokenized_chat.to('cuda'), max_new_tokens=200,)
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>>> tokenizer.decode(out[0][len(tokenized_chat[0]):])
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'Der deutsche Bundeskanzler ist Olaf Scholz.'
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```
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## Dataset
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The training data was split evenly between German and English based on the total number of tokens. We would like to thank [Disco Research](https://huggingface.co/DiscoResearch), [Jan Philipp Harries](https://huggingface.co/jphme), and [Björn Plüster](https://huggingface.co/bjoernp) for making their dataset available to us.
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**English and Code**
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- [Open-Hermes-2B](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
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**German**
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- [DiscoLM German Dataset](https://huggingface.co/DiscoResearch) includes the publicly available [germanrag](https://huggingface.co/datasets/DiscoResearch/germanrag) dataset
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- [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (German subset)
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- [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (German subset)
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## Training settings
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- Full instruction fine-tuning on 8xH100.
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- 0.6 - 4 training epochs (depending on dataset sampling).
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- Framework: [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
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- Precision: bf16
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- Optimizer: AdamW
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- Global batch size: 128 (with 8192 context length)
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- Cosine Annealing with Warmup
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## Tokenizer
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Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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## Evaluation
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Preliminary evaluation results can be found below.
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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.
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Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.
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<details>
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<summary>Evaluation results</summary>
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### All 5 Languages
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| | avg | arc_challenge | belebele | hellaswag | mmlu | truthfulqa |
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|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
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| Occiglot-7b-eu5 | 0.516895 | 0.508109 | 0.675556 | 0.718963 | 0.402064 | 0.279782 |
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| Occiglot-7b-eu5-instruct | 0.537799 | 0.53632 | 0.691111 | 0.731918 | 0.405198 | 0.32445 |
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| Occiglot-7b-de-en | 0.518337 | 0.496297 | 0.715111 | 0.669034 | 0.412545 | 0.298697 |
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| Occiglot-7b-de-en-instruct | 0.543173 | 0.530826 | 0.745778 | 0.67676 | 0.411326 | 0.351176 |
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| Leo-mistral-hessianai-7b | 0.484806 | 0.462103 | 0.653556 | 0.642242 | 0.379208 | 0.28692 |
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| Mistral-7b-v0.1 | 0.547111 | 0.528937 | 0.768444 | 0.682516 | 0.448253 | 0.307403 |
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| Mistral-7b-instruct-v0.2 | 0.56713 | 0.547228 | 0.741111 | 0.69455 | 0.422501 | 0.430262 |
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### English
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| | avg | arc_challenge | belebele | hellaswag | mmlu | truthfulqa |
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|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
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| Occiglot-7b-eu5 | 0.59657 | 0.530717 | 0.726667 | 0.789882 | 0.531904 | 0.403678 |
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| Occiglot-7b-eu5-instruct | 0.617905 | 0.558874 | 0.746667 | 0.799841 | 0.535109 | 0.449 |
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| Occiglot-7b-de-en | 0.518337 | 0.496297 | 0.715111 | 0.669034 | 0.412545 | 0.298697 |
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| Occiglot-7b-de-en-instruct | 0.543173 | 0.530826 | 0.745778 | 0.67676 | 0.411326 | 0.351176 |
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| Leo-mistral-hessianai-7b | 0.600949 | 0.522184 | 0.736667 | 0.777833 | 0.538812 | 0.429248 |
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| Mistral-7b-v0.1 | 0.668385 | 0.612628 | 0.844444 | 0.834097 | 0.624555 | 0.426201 |
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| Mistral-7b-instruct-v0.2 | 0.713657 | 0.637372 | 0.824444 | 0.846345 | 0.59201 | 0.668116 |
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### German
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| | avg | arc_challenge_de | belebele_de | hellaswag_de | mmlu_de | truthfulqa_de |
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|:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
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| Occiglot-7b-eu5 | 0.508311 | 0.493584 | 0.646667 | 0.666631 | 0.483406 | 0.251269 |
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| Occiglot-7b-eu5-instruct | 0.531506 | 0.529512 | 0.667778 | 0.685205 | 0.488234 | 0.286802 |
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| Occiglot-7b-de-en | 0.540085 | 0.50556 | 0.743333 | 0.67421 | 0.514633 | 0.26269 |
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| Occiglot-7b-de-en-instruct | 0.566474 | 0.54491 | 0.772222 | 0.688407 | 0.515915 | 0.310914 |
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| Leo-mistral-hessianai-7b | 0.517766 | 0.474765 | 0.691111 | 0.682109 | 0.488309 | 0.252538 |
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| Mistral-7b-v0.1 | 0.527957 | 0.476476 | 0.738889 | 0.610589 | 0.529567 | 0.284264 |
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| Mistral-7b-instruct-v0.2 | 0.535215 | 0.485885 | 0.688889 | 0.622438 | 0.501961 | 0.376904 |
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</details>
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## Acknowledgements
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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)).
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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)
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through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
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## License
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[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
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## See also
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- https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01
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- https://huggingface.co/NikolayKozloff/occiglot-7b-de-en-GGUF
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added_tokens.json
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{
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"<|im_end|>": 32001,
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"<|im_start|>": 32000
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}
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config.json
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{
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"_name_or_path": "./mayflowergmbh/occiglot-7b-de-en-instruct-hf",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.40.0.dev0",
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"use_cache": false,
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"vocab_size": 32002
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}
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qmodel.pt
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version https://git-lfs.github.com/spec/v1
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size 2597218112
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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size 493443
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tokenizer_config.json
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{
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"add_bos_token": true,
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3 |
+
"add_eos_token": false,
|
4 |
+
"added_tokens_decoder": {
|
5 |
+
"0": {
|
6 |
+
"content": "<unk>",
|
7 |
+
"lstrip": false,
|
8 |
+
"normalized": false,
|
9 |
+
"rstrip": false,
|
10 |
+
"single_word": false,
|
11 |
+
"special": true
|
12 |
+
},
|
13 |
+
"1": {
|
14 |
+
"content": "<s>",
|
15 |
+
"lstrip": false,
|
16 |
+
"normalized": false,
|
17 |
+
"rstrip": false,
|
18 |
+
"single_word": false,
|
19 |
+
"special": true
|
20 |
+
},
|
21 |
+
"2": {
|
22 |
+
"content": "</s>",
|
23 |
+
"lstrip": false,
|
24 |
+
"normalized": false,
|
25 |
+
"rstrip": false,
|
26 |
+
"single_word": false,
|
27 |
+
"special": true
|
28 |
+
},
|
29 |
+
"32000": {
|
30 |
+
"content": "<|im_start|>",
|
31 |
+
"lstrip": false,
|
32 |
+
"normalized": false,
|
33 |
+
"rstrip": false,
|
34 |
+
"single_word": false,
|
35 |
+
"special": false
|
36 |
+
},
|
37 |
+
"32001": {
|
38 |
+
"content": "<|im_end|>",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false,
|
43 |
+
"special": false
|
44 |
+
}
|
45 |
+
},
|
46 |
+
"additional_special_tokens": [],
|
47 |
+
"bos_token": "<s>",
|
48 |
+
"chat_template": "{{'<s>'}}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful assistant. Please give a long and detailed answer.' %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{{'<|im_start|>system\n' + system_message + '<|im_end|>\n'}}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
49 |
+
"clean_up_tokenization_spaces": false,
|
50 |
+
"eos_token": "</s>",
|
51 |
+
"legacy": true,
|
52 |
+
"model_max_length": 1000000000000000019884624838656,
|
53 |
+
"pad_token": "</s>",
|
54 |
+
"sp_model_kwargs": {},
|
55 |
+
"spaces_between_special_tokens": false,
|
56 |
+
"tokenizer_class": "LlamaTokenizer",
|
57 |
+
"unk_token": "<unk>",
|
58 |
+
"use_default_system_prompt": false
|
59 |
+
}
|