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--- |
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library_name: transformers |
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tags: [] |
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--- |
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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("AlexWortega/v5-it", |
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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("AlexWortega/v5-it") |
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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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