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--- |
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license: apache-2.0 |
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language: |
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- sw |
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- en |
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--- |
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```python |
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%%capture |
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# Installs Unsloth, Xformers (Flash Attention) and all other packages! |
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!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" |
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!pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes |
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from unsloth import FastLanguageModel |
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import torch |
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max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally! |
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dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ |
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load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. |
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model_name = "sartifyllc/sartify_gemma2-2B-16bit" |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = model_name, |
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max_seq_length = max_seq_length, |
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dtype = dtype, |
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trust_remote_code=True, |
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# load_in_4bit = load_in_4bit, |
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# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf |
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) |
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alpaca_prompt = """Hapo chini kuna maelezo ya kazi, pamoja na maelezo ya ziada yanayotoa muktadha zaidi. Andika jibu ambalo linakamilisha ombi hilo ipasavyo. |
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### Maelezo: |
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{} |
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### Ziada: |
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{} |
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### Jibu: |
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{}""" |
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
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# alpaca_prompt = Copied from above |
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inputs = tokenizer( |
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[ |
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alpaca_prompt.format( |
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"Rudia tu kila kitu ninachosema kwa Kiingereza kwa Kiswahili wala usiseme chochote kingine.", # instruction |
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"Who is the president of Tanzania?", # 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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from transformers import TextStreamer |
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text_streamer = TextStreamer(tokenizer) |
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128) |
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``` |
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