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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- persimmon |
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--- |
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# perSLIMmon-8b-base |
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> persimmon-8b went to the vocab lipo clinic |
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A slimmed-down version of [persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base) which removes the ~70,000 unused entries in the model vocabulary and tokenizer (see the safetensors layer overview). Should be _slightly_ faster. |
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Credit: [fine-tune-fuyu](https://github.com/phillip-kravtsov/fine-tune-fuyu) (`scripts/surgery.py` was adapted for persimmon) |
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## inference |
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install required pkgs: |
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```sh |
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pip install -U transformers accelerate bitsandbytes sentencepiece |
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``` |
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load in 4bit & run inference: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("pszemraj/perSLIMmon-8b-base") |
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model = AutoModelForCausalLM.from_pretrained( |
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"pszemraj/perSLIMmon-8b-base", |
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load_in_4bit=True, # GPU required |
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torch_dtype="auto", |
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device_map="auto", |
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) |
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inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to( |
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model.device |
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) |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=64, |
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temperature=0.75, |
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top_p=0.95, |
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epsilon_cutoff=1e-5, |
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repetition_penalty=1.05, |
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renormalize_logits=True, |
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do_sample=True, |
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) # adapt inference params as needed |
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print(tokenizer.decode(tokens[0], skip_special_tokens=True)) |
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``` |
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inference is decently fast on a colab T4: |
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``` |
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CPU times: user 6.01 s, sys: 138 ms, total: 6.15 s |
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Wall time: 6.23 s |
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``` |
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