57 lines
2.1 KiB
Markdown
57 lines
2.1 KiB
Markdown
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---
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library_name: transformers
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language:
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- ru
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- en
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---
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# Релиз вихря 0.3-0.4
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Долили сильно больше данных в sft, теперь стабильнее работает json и multiturn, слегка подточили параметры претрена модели
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Added a lot more data to sft, now json and multiturn work more stable on long context and hard prompts
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- [Google Colab](https://colab.research.google.com/drive/15O9LwZhVUa1LWhZa2UKr_B-KOKenJBvv#scrollTo=5EeNFU2-9ERi)
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- [GGUF](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4-GGUF)
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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 = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4",
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device_map="auto",
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attn_implementation="flash_attention_2",
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torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4")
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from transformers import AutoTokenizer, pipeline
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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prompts = [
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"В чем разница между фруктом и овощем?",
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"Годы жизни колмагорова?"]
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def test_inference(prompt):
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prompt = pipe.tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True)
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print(prompt)
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outputs = pipe(prompt, max_new_tokens=512, do_sample=True, num_beams=1, temperature=0.25, top_k=50, top_p=0.98, eos_token_id=79097)
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return outputs[0]['generated_text'][len(prompt):].strip()
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for prompt in prompts:
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print(f" prompt:\n{prompt}")
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print(f" response:\n{test_inference(prompt)}")
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print("-"*50)
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```
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```
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@article{nikolich2024vikhr,
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title={Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian},
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author={Aleksandr Nikolich and Konstantin Korolev and Artem Shelmanov},
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journal={arXiv preprint arXiv:2405.13929},
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year={2024},
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url={https://arxiv.org/pdf/2405.13929}
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}
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```
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