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Model: ilsp/Meltemi-7B-Instruct-v1.5 Source: Original Platform
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---
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language:
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- el
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- finetuned
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inference: true
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---
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# 🚨 **CONSIDER USING [Krikri 8B Instruct](https://huggingface.co/ilsp/Llama-Krikri-8B-Instruct), OUR NEWEST INSTRUCT MODEL WHICH OUTPERFORMS MELTEMI BY +34.8% ON [GREEK IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek)!** 🚨
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# Meltemi Instruct Large Language Model for the Greek language
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We present Meltemi 7B Instruct v1.5 Large Language Model (LLM), a new and improved instruction fine-tuned version of [Meltemi 7B v1.5](https://huggingface.co/ilsp/Meltemi-7B-v1.5).
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# Model Information
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- Vocabulary extension of the Mistral 7b tokenizer with Greek tokens for lower costs and faster inference (**1.52** vs. 6.80 tokens/word for Greek)
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- 8192 context length
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- Fine-tuning has been done with the [Odds Ratio Preference Optimization (ORPO)](https://arxiv.org/abs/2403.07691) algorithm using 97k preference data:
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* 89,730 Greek preference data which are mostly translated versions of high-quality datasets on Hugging Face
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* 7,342 English preference data
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- Our alignment procedure is based on the [TRL - Transformer Reinforcement Learning](https://huggingface.co/docs/trl/index) library and partially on the [Hugging Face finetuning recipes](https://github.com/huggingface/alignment-handbook)
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# Instruction format
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The prompt format is the same as the [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) format and can be
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utilized through the tokenizer's [chat template](https://huggingface.co/docs/transformers/main/chat_templating) functionality as follows:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("ilsp/Meltemi-7B-Instruct-v1.5")
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tokenizer = AutoTokenizer.from_pretrained("ilsp/Meltemi-7B-Instruct-v1.5")
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model.to(device)
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messages = [
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{"role": "system", "content": "Είσαι το Μελτέμι, ένα γλωσσικό μοντέλο για την ελληνική γλώσσα. Είσαι ιδιαίτερα βοηθητικό προς την χρήστρια ή τον χρήστη και δίνεις σύντομες αλλά επαρκώς περιεκτικές απαντήσεις. Απάντα με προσοχή, ευγένεια, αμεροληψία, ειλικρίνεια και σεβασμό προς την χρήστρια ή τον χρήστη."},
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{"role": "user", "content": "Πες μου αν έχεις συνείδηση."},
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]
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# Through the default chat template this translates to
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#
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# <|system|>
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# Είσαι το Μελτέμι, ένα γλωσσικό μοντέλο για την ελληνική γλώσσα. Είσαι ιδιαίτερα βοηθητικό προς την χρήστρια ή τον χρήστη και δίνεις σύντομες αλλά επαρκώς περιεκτικές απαντήσεις. Απάντα με προσοχή, ευγένεια, αμεροληψία, ειλικρίνεια και σεβασμό προς την χρήστρια ή τον χρήστη.</s>
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# <|user|>
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# Πες μου αν έχεις συνείδηση.</s>
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# <|assistant|>
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#
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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input_prompt = tokenizer(prompt, return_tensors='pt').to(device)
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outputs = model.generate(input_prompt['input_ids'], max_new_tokens=256, do_sample=True)
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print(tokenizer.batch_decode(outputs)[0])
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# Ως μοντέλο γλώσσας AI, δεν έχω τη δυνατότητα να αντιληφθώ ή να βιώσω συναισθήματα όπως η συνείδηση ή η επίγνωση. Ωστόσο, μπορώ να σας βοηθήσω με οποιεσδήποτε ερωτήσεις μπορεί να έχετε σχετικά με την τεχνητή νοημοσύνη και τις εφαρμογές της.
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messages.extend([
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{"role": "assistant", "content": tokenizer.batch_decode(outputs)[0]},
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{"role": "user", "content": "Πιστεύεις πως οι άνθρωποι πρέπει να φοβούνται την τεχνητή νοημοσύνη;"}
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])
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# Through the default chat template this translates to
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#
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# <|system|>
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# Είσαι το Μελτέμι, ένα γλωσσικό μοντέλο για την ελληνική γλώσσα. Είσαι ιδιαίτερα βοηθητικό προς την χρήστρια ή τον χρήστη και δίνεις σύντομες αλλά επαρκώς περιεκτικές απαντήσεις. Απάντα με προσοχή, ευγένεια, αμεροληψία, ειλικρίνεια και σεβασμό προς την χρήστρια ή τον χρήστη.</s>
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# <|user|>
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# Πες μου αν έχεις συνείδηση.</s>
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# <|assistant|>
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# Ως μοντέλο γλώσσας AI, δεν έχω τη δυνατότητα να αντιληφθώ ή να βιώσω συναισθήματα όπως η συνείδηση ή η επίγνωση. Ωστόσο, μπορώ να σας βοηθήσω με οποιεσδήποτε ερωτήσεις μπορεί να έχετε σχετικά με την τεχνητή νοημοσύνη και τις εφαρμογές της.</s>
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# <|user|>
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# Πιστεύεις πως οι άνθρωποι πρέπει να φοβούνται την τεχνητή νοημοσύνη;</s>
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# <|assistant|>
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#
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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input_prompt = tokenizer(prompt, return_tensors='pt').to(device)
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outputs = model.generate(input_prompt['input_ids'], max_new_tokens=256, do_sample=True)
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print(tokenizer.batch_decode(outputs)[0])
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```
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Please make sure that the BOS token is always included in the tokenized prompts. This might not be the default setting in all evaluation or fine-tuning frameworks.
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# Evaluation
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The evaluation suite we created includes 6 test sets and has been implemented based on a [fork](https://github.com/LeonVouk/lighteval) of the [lighteval](https://github.com/huggingface/lighteval) framework.
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Our evaluation suite includes:
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* Four machine-translated versions ([ARC Greek](https://huggingface.co/datasets/ilsp/arc_greek), [Truthful QA Greek](https://huggingface.co/datasets/ilsp/truthful_qa_greek), [HellaSwag Greek](https://huggingface.co/datasets/ilsp/hellaswag_greek), [MMLU Greek](https://huggingface.co/datasets/ilsp/mmlu_greek)) of established English benchmarks for language understanding and reasoning ([ARC Challenge](https://arxiv.org/abs/1803.05457), [Truthful QA](https://arxiv.org/abs/2109.07958), [Hellaswag](https://arxiv.org/abs/1905.07830), [MMLU](https://arxiv.org/abs/2009.03300)).
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* An existing benchmark for question answering in Greek ([Belebele](https://arxiv.org/abs/2308.16884))
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* A novel benchmark created by the ILSP team for medical question answering based on the medical exams of [DOATAP](https://www.doatap.gr) ([Medical MCQA](https://huggingface.co/datasets/ilsp/medical_mcqa_greek)).
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Our evaluation is performed in a few-shot setting, consistent with the settings in the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
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We can see that our new training and fine-tuning procedure for Meltemi 7B Instruct v1.5 enhances performance across all Greek test sets by a **+7.8%** average improvement compared to the earlier Meltemi Instruct 7B v1 model. The results for the Greek test sets are shown in the following table:
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| | Medical MCQA EL (15-shot) | Belebele EL (5-shot) | HellaSwag EL (10-shot) | ARC-Challenge EL (25-shot) | TruthfulQA MC2 EL (0-shot) | MMLU EL (5-shot) | **Average** |
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|----------------|----------------|-------------|--------------|------------------|-------------------|---------|---------|
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| Mistral 7B | 29.8% | 45.0% | 36.5% | 27.1% | 45.8% | 35% | **36.5%** |
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| Meltemi 7B Instruct v1 | 36.1% | 56.0% | 59.0% | 44.4% | 51.1% | 34.1% | **46.8%** |
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| Meltemi 7B Instruct v1.5 | 48.0% | 75.5% | 63.7% | 40.8% | 53.8% | 45.9% | **54.6%** |
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# Ethical Considerations
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This model has been aligned with human preferences, but might generate misleading, harmful, and toxic content.
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# Acknowledgements
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The ILSP team utilized Amazon’s cloud computing services, which were made available via GRNET under the [OCRE Cloud framework](https://www.ocre-project.eu/), providing Amazon Web Services for the Greek Academic and Research Community.
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# Citation
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```
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@misc{voukoutis2024meltemiopenlargelanguage,
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title={Meltemi: The first open Large Language Model for Greek},
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author={Leon Voukoutis and Dimitris Roussis and Georgios Paraskevopoulos and Sokratis Sofianopoulos and Prokopis Prokopidis and Vassilis Papavasileiou and Athanasios Katsamanis and Stelios Piperidis and Vassilis Katsouros},
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year={2024},
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eprint={2407.20743},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2407.20743},
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}
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```
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added_tokens.json
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config.json
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{
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"_name_or_path": "/opt/volume/greek_llm/meltemi_chat/meltemi-orpo-last-chance/checkpoint-2484/",
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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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"head_dim": 128,
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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": "bfloat16",
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"transformers_version": "4.43.2",
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"use_cache": false,
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"vocab_size": 61384
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}
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||||
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|
||||
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|
||||
}
|
||||
}
|
||||
63
special_tokens_map.json
Normal file
63
special_tokens_map.json
Normal file
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|system|>",
|
||||
"<|user|>",
|
||||
"<|assistant|>",
|
||||
"<|context|>",
|
||||
"<|/context|>",
|
||||
"[INST]",
|
||||
"[/INST]",
|
||||
"<|translate-en-el|>",
|
||||
"<|translate-el-en|>",
|
||||
"<|tldr|>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"cls_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"mask_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sep_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
171024
tokenizer.json
Normal file
171024
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:531c02ac6d66e23eb0269bc8c396094abc612491af242b731cf0ffb42a769aa4
|
||||
size 1180082
|
||||
147
tokenizer_config.json
Normal file
147
tokenizer_config.json
Normal file
@@ -0,0 +1,147 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": true,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61366": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61367": {
|
||||
"content": "<|system|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61368": {
|
||||
"content": "<|user|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61369": {
|
||||
"content": "<|assistant|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61370": {
|
||||
"content": "<|context|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61371": {
|
||||
"content": "<|/context|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61372": {
|
||||
"content": "[INST]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61373": {
|
||||
"content": "[/INST]",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61374": {
|
||||
"content": "<|translate-en-el|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61375": {
|
||||
"content": "<|translate-el-en|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
},
|
||||
"61376": {
|
||||
"content": "<|tldr|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|system|>",
|
||||
"<|user|>",
|
||||
"<|assistant|>",
|
||||
"<|context|>",
|
||||
"<|/context|>",
|
||||
"[INST]",
|
||||
"[/INST]",
|
||||
"<|translate-en-el|>",
|
||||
"<|translate-el-en|>",
|
||||
"<|tldr|>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"cls_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"mask_token": "<unk>",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<pad>",
|
||||
"sep_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false,
|
||||
"use_fast": true
|
||||
}
|
||||
Reference in New Issue
Block a user