NickyNicky
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Update README.md
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README.md
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tags:
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- mergekit
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- merge
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-
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---
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# merged
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density: 0.55
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weight: 0.56
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```
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tags:
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- mergekit
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- merge
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license: apache-2.0
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---
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# merged
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density: 0.55
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weight: 0.56
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```
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```Python
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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HfArgumentParser,
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TrainingArguments,
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pipeline,
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logging,
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GenerationConfig,
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TextIteratorStreamer,
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)
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import torch
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new_model= "NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_all_Cluster_merge_v1"
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model = AutoModelForCausalLM.from_pretrained(#f'NickyNicky/{new_model}',
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new_model,
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device_map="auto",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage= True,
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# use_flash_attention_2=False,
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)
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tokenizer = AutoTokenizer.from_pretrained(new_model,
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max_length=2048,
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trust_remote_code=True,
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use_fast = True,
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)
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tokenizer.pad_token = tokenizer.eos_token
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# tokenizer.padding_side = 'left'
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tokenizer.padding_side = 'right'
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prompt= """<|im_start|>system
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You are a helpful AI assistant.<|im_end|>
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<|im_start|>user
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escribe una historia de amor.<|im_end|>
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<|im_start|>assistant
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"""
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inputs = tokenizer.encode(prompt,
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return_tensors="pt",
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add_special_tokens=False).cuda()#.to("cuda") # False # True
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generation_config = GenerationConfig(
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max_new_tokens=700,
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temperature=0.5,
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top_p=0.9,
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top_k=40,
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repetition_penalty=1.1, #1.1, # 1.0 means no penalty, > 1.0 means penalty, 1.2 from CTRL paper
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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outputs = model.generate(
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generation_config=generation_config,
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input_ids=inputs,)
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# tokenizer.decode(outputs[0], skip_special_tokens=False) #True
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print(tokenizer.decode(outputs[0], skip_special_tokens=False))
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```
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