Improve language tag
Browse filesHi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.
README.md
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---
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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- Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
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tags:
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- merge
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- mergekit
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- lazymergekit
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- Qwen/Qwen2.5-1.5B-Instruct
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- Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
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license: apache-2.0
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datasets:
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- multilingual/orca_dpo_pairs
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- Kukedlc/Big-Spanish-1.2M
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language:
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---
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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- Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
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tags:
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- merge
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- mergekit
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- lazymergekit
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- Qwen/Qwen2.5-1.5B-Instruct
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- Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
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license: apache-2.0
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datasets:
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- multilingual/orca_dpo_pairs
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- Kukedlc/Big-Spanish-1.2M
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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---
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# NeuralQwen-2.5-1.5B-Spanish
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NeuralQwen-2.5-1.5B-Spanish is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
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* [Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO](https://huggingface.co/Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO)
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## 🧩 Configuration
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```yaml
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models:
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- model: Qwen/Qwen2.5-1.5B
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# No parameters necessary for base model
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- model: Qwen/Qwen2.5-1.5B-Instruct
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parameters:
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density: 0.66
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weight: 0.6
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- model: Kukedlc/Qwen2.5-1.5B-Spanish-1.0-DPO
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parameters:
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density: 0.44
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weight: 0.4
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merge_method: dare_ties
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base_model: Qwen/Qwen2.5-1.5B
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parameters:
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int8_mask: true
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dtype: float16
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "Kukedlc/NeuralQwen-2.5-1.5B-Spanish"
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messages = [{"role": "system", "content": "Eres un asistente de pensamiento logico que piensa paso a paso, por cada pregunta que te hagan deberes comprobar la respuesta por 3 metodos diferentes."},
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{"role": "user", "content": "Cuantas letras 'r' tiene la palabra strawberry?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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