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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- gemma |
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- trl |
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base_model: unsloth/gemma-7b-bnb-4bit |
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--- |
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# Uploaded model |
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- **Developed by:** Andyrasika |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit |
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This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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```py |
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if False: |
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from unsloth import FastLanguageModel |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = "Andyrasika/lora_gemma", |
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max_seq_length = max_seq_length, |
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dtype = dtype, |
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load_in_4bit = load_in_4bit, |
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) |
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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{} |
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### Input: |
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{} |
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### Response: |
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{}""" |
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inputs = tokenizer( |
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[ |
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alpaca_prompt.format( |
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"What is a famous tall tower in Paris?", # instruction |
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"", # input |
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"", # output - leave this blank for generation! |
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) |
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], return_tensors = "pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) |
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tokenizer.batch_decode(outputs) |
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``` |
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Output |
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``` |
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['<bos>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the |
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request.\n\n### Instruction:\nWhat is a famous tall tower in Paris?\n\n### Input:\n\n\n### Response:\nOne of the most famous tall towers in Paris is the Eiffel Tower. |
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It is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower. |
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The tower is 324 meters (1,063 feet'] |
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``` |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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[notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing#scrollTo=FqfebeAdT073) |
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