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Model: ytu-ce-cosmos/Turkish-Gemma-9b-v0.1 Source: Original Platform
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README.md
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README.md
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---
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license: gemma
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language:
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- tr
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pipeline_tag: text-generation
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base_model: google/gemma2-9b
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tags:
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- Turkish
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- gemma2
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- DPO
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- SFT
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- conversational
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- instruction
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---
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<img src="./Turkish_Gemma.png"/>
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# Turkish-Gemma-9b-v0.1
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This is the Turkish-Gemma-9b-v0.1. This model is based on Gemma-2-9b, and was developed through a combination of continual pre-training, supervised fine-tuning (SFT), direct preference optimization (DPO), and model merging.
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The Turkish-Gemma-9b-v0.1 is designed for Turkish text generation tasks, providing coherent, contextually relevant continuations and answers. Due to the diverse nature of the training data—which includes large-scale pre-training corpora, instruction-tuning data, and human preference data—the model may exhibit biases. Users should be aware of these and deploy the model responsibly.
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You can easily demo the model here (Coming soon!): https://cosmos.yildiz.edu.tr/cosmosllm
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To evaluate model performance, we compiled a dataset of 1,450 carefully designed questions across diverse categories. Each question was reviewed and rated by 18 human annotators, allowing for a reliable comparison across multiple models.
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The table below summarizes the evaluation results:
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### 🏆 Model Comparison: Win Rates
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| Model Name | Win Rate |
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|---------------------------------------------|-----------------|
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| Qwen/Qwen3-30B-A3B | 62.39% |
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| gpt-4o-mini | 62.12% |
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| google/gemma-3-12b-it | 61.61% |
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| google/gemma-2-27b-it | 57.91% |
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| **ytu-ce-cosmos/Turkish-Gemma-9b-v0.1** | **57.30%** |
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| google/gemma-2-9b-it | 54.13% |
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| ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1 | 36.89% |
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### Voting Metodology
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A question and two answers from different models were presented to human judges. The judges selected the better answer based on their preferences. For example, in the question below, the judge selected the answer on the right:
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### 📊 Turkish Evaluation Benchmark Results (via `malhajar17/lm-evaluation-harness_turkish`)
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| Model Name | Average | MMLU | Truthful_QA | ARC | Hellaswag | Gsm8K | Winogrande |
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|---------------------------------------------|---------|-------|--------------|-------|-----------|-------|------------|
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| Qwen/Qwen2.5-72B-Instruct | 67.69 | 77.28 | 59.86 | 61.52 | 61.98 | 83.6 | 61.92 |
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| google/gemma-3-27b-it | 67.36 | 70.2 | 57.06 | 66.98 | 66.58 | 77.52 | 65.8 |
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| google/gemma-2-27b-it | 65.57 | 66.49 | 57.45 | 63.65 | 63.86 | 76.54 | 65.4 |
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| meta-llama/Llama-3-1-70B-Instruct | 63.92 | 74.00 | 51.41 | 59.64 | 64.31 | 66.13 | 66.90 |
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| Qwen/Qwen2.5-32B-Instruct | 63.74 | 70.93 | 57.87 | 57.00 | 57.04 | 77.83 | 61.77 |
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| **ytu-ce-cosmos/Turkish-Gemma-9b-v0.1** | **63.31** | **63.85** | **54.21** | **59.64** | **64.19** | **73.42** | **64.53** |
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| google/gemma-3-12b-it | 62.94 | 63.92 | 57.16 | 60.67 | 62.00 | 72.06 | 61.77 |
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| Qwen/Qwen2.5-14B-it | 60.34 | 65.28 | 59.00 | 50.00 | 52.22 | 76.77 | 58.77 |
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| google/gemma-2-9b-it | 59.14 | 61.07 | 55.77 | 56.31 | 56.48 | 63.10 | 62.09 |
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| ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1 | 55.03 | 51.97 | 57.56 | 51.02 | 52.96 | 59.87 | 57.77 |
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| Qwen/Qwen2.5-7B-Instruct | 53.42 | 56.31 | 55.99 | 42.06 | 44.71 | 64.16 | 59.66 |
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#### Transformers pipeline
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```python
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import transformers
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import torch
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model_id = "ytu-ce-cosmos/Turkish-Gemma-9b-v0.1"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "İsmi RD olan bir fonksiyon ona verilen sayının çarpmaya göre tersini döndürmektedir. Örneğin RD(3)=1/3. Buna göre RD(X)=X ifadesini doğru yapan kaç X değeri vardır?"}
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]
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terminators = [
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pipeline.tokenizer.eos_token_id,
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pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
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]
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outputs = pipeline(
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messages,
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max_new_tokens=512,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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)
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print(outputs[0]["generated_text"][-1])
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# RD(X) = X ifadesi, bir sayının çarpmaya göre tersinin kendisiyle eşit olması anlamına gelir. Yani, X ile 1/X aynı olmalıdır. Bu durum yalnızca X'in karesi 1 olduğunda gerçekleşir:
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# X² = 1
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# Bu denklemin çözümleri:
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# X = 1 ve X = -1
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# Dolayısıyla, RD(X) = X eşitliğini sağlayan *iki* X değeri vardır: *1* ve *-1*.
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```
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#### Transformers AutoModelForCausalLM
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "ytu-ce-cosmos/Turkish-Gemma-9b-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "İsmi RD olan bir fonksiyon ona verilen sayının çarpmaya göre tersini döndürmektedir. Örneğin RD(3)=1/3. Buna göre RD(X)=X ifadesini doğru yapan kaç X değeri vardır?"}
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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terminators = [
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pipeline.tokenizer.eos_token_id,
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pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
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]
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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eos_token_id=terminators,
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do_sample=False,
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)
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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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# RD(X) = X ifadesi, bir sayının çarpmaya göre tersinin kendisiyle eşit olması anlamına gelir. Yani, X ile 1/X aynı olmalıdır. Bu durum yalnızca X'in karesi 1 olduğunda gerçekleşir:
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# X² = 1
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# Bu denklemin çözümleri:
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# X = 1 ve X = -1
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# Dolayısıyla, RD(X) = X eşitliğini sağlayan *iki* X değeri vardır: *1* ve *-1*.
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```
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# Acknowledgments
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- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
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- Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant numbers 1016912023 and
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1018512024
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### Contact
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COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department <br>
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https://cosmos.yildiz.edu.tr/ <br>
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cosmos@yildiz.edu.tr
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---
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license: gemma2
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---
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Turkish_Gemma.png
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Turkish_Gemma.png
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config.json
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{
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 3584,
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"max_position_embeddings": 8192,
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"sliding_window_size": 4096,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": true,
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"vocab_size": 256000
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}
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||||
}
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||||
}
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<start_of_turn>",
|
||||
"<end_of_turn>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<eos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
|
||||
size 17525357
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
||||
size 4241003
|
||||
2013
tokenizer_config.json
Normal file
2013
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user