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--- |
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license: apache-2.0 |
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datasets: |
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- Open-Orca/OpenOrca |
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- OpenAssistant/oasst_top1_2023-08-25 |
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language: |
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- bg |
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- ca |
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- cs |
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- da |
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- de |
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- en |
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- es |
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- fr |
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- hr |
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- hu |
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- it |
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- nl |
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- pl |
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- pt |
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- ro |
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- ru |
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- sl |
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- sr |
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- sv |
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- uk |
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library_name: transformers |
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--- |
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 |
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``` |
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reference-data-model: |
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datasets: |
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- OpenAssistant/oasst_top1_2023-08-25: |
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lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" |
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link: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25 |
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model: |
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- Open-Orca/Mistral-7B-OpenOrca |
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Link: |
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https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca |
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100 examples of generating: |
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- Link: |
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https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2/blob/main/output.xlsx |
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Activated training with: |
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- Link: |
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https://huggingface.co/blog/tomaarsen/attention-sinks |
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https://github.com/tomaarsen/attention_sinks |
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https://arxiv.org/abs/2309.17453 |
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Version: |
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- Link: |
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https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 |
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https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3 |
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``` |
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## |
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```py |
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# attention-sinks |
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pip install attention_sinks |
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# flash-attn |
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!export CUDA_HOME=/usr/local/cuda-11.8 |
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!MAX_JOBS=4 pip install flash-attn --no-build-isolation -qqq |
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!pip install git+"https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary" -qqq |
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``` |
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## Version |
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```py |
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import torch, transformers,torchvision |
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torch.__version__,transformers.__version__, torchvision.__version__ |
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#OUTPUTS: ('2.0.1+cu118', '4.34.0.dev0', '0.15.2+cu118') |
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``` |
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## How to use |
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```py |
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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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from attention_sinks import AutoModelForCausalLM |
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import torch |
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# model_id = 'Open-Orca/Mistral-7B-OpenOrca' |
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model_id='NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2' |
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model = AutoModelForCausalLM.from_pretrained(model_id, |
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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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load_in_4bit=True, |
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low_cpu_mem_usage= True, |
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attention_sink_size=4, |
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attention_sink_window_size=1024, #512, # <- Low for the sake of faster generation |
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) |
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max_length=2048 |
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print("max_length",max_length) |
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tokenizer = AutoTokenizer.from_pretrained(model_id, |
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# use_fast = False, |
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max_length=max_length,) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = 'right' |
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#EXAMPLE #1 |
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txt="""<|im_start|>user |
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I'm looking for an efficient Python script to output prime numbers. Can you help me out? I'm interested in a script that can handle large numbers and output them quickly. Also, it would be great if the script could take a range of numbers as input and output all the prime numbers within that range. Can you generate a script that fits these requirements? Thanks!<|im_end|> |
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<|im_start|>assistant |
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""" |
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#EXAMPLE #2 |
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txt="""<|im_start|>user |
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Estoy desarrollando una REST API con Nodejs, y estoy tratando de aplicar algún sistema de seguridad, ya sea con tokens o algo similar, me puedes ayudar?<|im_end|> |
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<|im_start|>assistant |
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""" |
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inputs = tokenizer.encode(txt, return_tensors="pt").to("cuda") |
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generation_config = GenerationConfig( |
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max_new_tokens=max_new_tokens, |
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temperature=0.7, |
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top_p=0.9, |
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top_k=len_tokens, |
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repetition_penalty=1.11, |
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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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# use_cache=True, |
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# stopping_criteria= StoppingCriteriaList([stopping_criteria]), |
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) |
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outputs = model.generate(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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``` |
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