Initial upload of the model

.gitignore

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+.ipynb_checkpoints

README.md

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 ---
-license: apache-2.0
+base_model: Unbabel/TowerInstruct-7B-v0.1
+license: cc-by-nc-4.0
+language:
+- tt
+- en
+- de
+- fr
+- zh
+- pt
+- nl
+- ru
+- ko
+- it
+- es
+tags:
+- tweety
+datasets:
+- oscar-corpus/OSCAR-2301
 ---
+
+# Tweety Tatar / Hydra-Base 7b / 2024-v1
+
+## Model description
+This model is our Hydra LLM for the [Tatar language](https://en.wikipedia.org/wiki/Tatar_language), finetuned from the [TowerInstruct-7b-v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1) model trained by Unbabel. 
+Hydra LLMs are trans-tokenized language models finetuned to produce output in a particular language, while accepting input encoded using either their own tokenizer, the one of their base model, or a mix of both. 
+This enables them to receive code-switched input in both their native language and other languages, which is an ideal setup for translation tasks, or retrieval-augmented generation (RAG) in cross-lingual scenarios.
+
+## In-scope usage
+This model can be used as-is to answer questions in Tatar based on a cross-lingual context, or finetuned into a machine translation system from one of the 10 languages supported by TowerInstruct into the Tatar language. 
+This list of languages nobably includes English and Russian. 
+The model performs best when translating sentences or small paragraphs, and is not suited for document translation tasks. 
+This model should not be used in the reverse direction, to translate Tatar into English. 
+When the system isn't finetuned, enabling beam search is recommended for best results. 
+We also provide a model [finetuned for translation](https://huggingface.co/Tweeties/tweety-tatar-hydra-trans-7b-2024-v1), but take note of the non-commercial license imposed by Unbabel on the base model.
+
+## Usage instructions
+Using this model usually requires building the prompts by mixing tokens from two tokenizers, the original TowerInstruct tokenizer for input in the source language, and the new Tatar tokenizer for the prompt and output, as described in the examples below:
+
+```py
+import re
+import torch
+import torch.nn as nn
+import transformers
+
+MODEL_NAME = "Tweeties/tweety-tatar-hydra-base-7b-2024-v1"
+MAIN_TOKENIZER_NAME = "Tweeties/tweety-tatar-hydra-base-7b-2024-v1"
+UTIL_TOKENIZER_NAME = "Unbabel/TowerInstruct-7B-v0.1"
+
+model = transformers.AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)
+main_tokenizer = transformers.LlamaTokenizerFast.from_pretrained(MAIN_TOKENIZER_NAME)
+util_tokenizer = transformers.LlamaTokenizerFast.from_pretrained(UTIL_TOKENIZER_NAME)
+
+main_tokenizer_len = len(main_tokenizer)
+```
+
+
+### Cross-lingual question answering
+
+```py
+def answer_english_question(english_text: str) -> str:
+    
+    # craft the input
+    input_ids = torch.concat([
+        main_tokenizer.encode(f"Татар телендә түбәндәге сорауга җавап бирегез:\n", return_tensors='pt'),
+        util_tokenizer.encode(f"{english_text}", add_special_tokens=False, return_tensors='pt') + torch.tensor([main_tokenizer_len]),
+        main_tokenizer.encode(f"\n\nҗавап:\n", add_special_tokens=False, return_tensors='pt')
+    ], axis=1)
+
+    # prevent the model from repeating the prompt
+    prompt_starts = [
+        main_tokenizer.encode("Түбәндәге"), 
+        main_tokenizer.encode("\nТүбәндәге")[2:], 
+        main_tokenizer.encode("Текстны"), 
+        main_tokenizer.encode("\nТекстны")[2:]
+    ]
+
+    # prevent the model from repeating the English text
+    english_starts = [
+        main_tokenizer.encode(re.sub(r'[ ].*', '', english_text)), 
+        main_tokenizer.encode('\n'+re.sub(r'[ ].*', '', english_text))[2:], 
+        main_tokenizer.encode(re.sub(r'[ ].*', '', english_text.upper())),
+        main_tokenizer.encode('\n'+re.sub(r'[ ].*', '', english_text.upper()))[2:],
+    ]
+    
+    # genereate the output
+    model_inputs = {'input_ids':input_ids.to(model.device)}
+    model_outputs = model.generate(
+        **model_inputs,
+        max_new_tokens=5,
+        num_beams=8,
+        no_repeat_ngram_size=6,
+        early_stopping=False,
+        pad_token_id=main_tokenizer.eos_token_id,
+        eos_token_id=main_tokenizer.convert_tokens_to_ids(['<0x0A>','</s>']),
+        bad_words_ids=english_starts+prompt_starts
+    )
+
+    # decode the output
+    return (main_tokenizer.decode(model_outputs[0][input_ids.shape[1]:]))
+
+answer_english_question("Is Paris located in France?\n") # Әйе, Парижда
+```
+
+### Machine Translation (see [finetuned model](https://huggingface.co/Tweeties/tweety-tatar-hydra-trans-7b-2024-v1))
+
+```py
+def translate_english_text(english_text: str) -> str:
+    
+    # craft the input
+    input_ids = torch.concat([
+        main_tokenizer.encode(f"Түбәндәге текстны инглиз теленнән татар теленә тәрҗемә итегез:\n", return_tensors='pt'),
+        util_tokenizer.encode(f"{english_text}", add_special_tokens=False, return_tensors='pt') + torch.tensor([main_tokenizer_len]),
+        main_tokenizer.encode(f"\nТекстны татар теленә тәрҗемә итү:\n", add_special_tokens=False, return_tensors='pt')
+    ], axis=1)
+
+    # prevent the model from repeating the prompt
+    prompt_starts = [
+        main_tokenizer.encode("Түбәндәге"), 
+        main_tokenizer.encode("\nТүбәндәге")[2:], 
+        main_tokenizer.encode("Текстны"), 
+        main_tokenizer.encode("\nТекстны")[2:]
+    ]
+
+    # prevent the model from repeating the English text
+    english_starts = [
+        main_tokenizer.encode(re.sub(r'[ ].*', '', english_text)), 
+        main_tokenizer.encode('\n'+re.sub(r'[ ].*', '', english_text))[2:], 
+        main_tokenizer.encode(re.sub(r'[ ].*', '', english_text.upper())),
+        main_tokenizer.encode('\n'+re.sub(r'[ ].*', '', english_text.upper()))[2:],
+    ]
+    
+    # genereate the output
+    model_inputs = {'input_ids':input_ids.to(model.device)}
+    model_outputs = model.generate(
+        **model_inputs,
+        max_new_tokens=128,
+        num_beams=8,
+        no_repeat_ngram_size=6,
+        early_stopping=False,
+        pad_token_id=main_tokenizer.eos_token_id,
+        eos_token_id=main_tokenizer.convert_tokens_to_ids(['<0x0A>','</s>']),
+        bad_words_ids=english_starts+prompt_starts
+    )
+
+    # decode the output
+    return (main_tokenizer.decode(model_outputs[0][input_ids.shape[1]:]))
+
+translate_english_text("The city of Paris is very pretty.") # Париж шәһәре бик матур.
+```

config.json

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+{
+  "_name_or_path": "Tweeties/tweety-tatar-hydra-base-7b-2024-v1",
+  "additional_special_tokens_ids": [],
+  "architectures": [
+    "LlamaHydraForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_llama_hydra.LlamaHydraConfig",
+    "AutoModelForCausalLM": "modeling_llama_hydra.LlamaHydraForCausalLM"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "cls_token_id": null,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "input_vocab_size": 64007,
+  "intermediate_size": 11008,
+  "mask_token_id": null,
+  "max_position_embeddings": 4096,
+  "model_type": "llama_hydra",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "output_vocab_size": 32000,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.1",
+  "unk_token_id": 0,
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.37.1"
+}

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