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--- |
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tags: |
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- autotrain |
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- text-generation-inference |
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- text-generation |
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- peft |
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library_name: transformers |
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base_model: Arthur-LAGACHERIE/Gemma-2-2b-4bit |
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widget: |
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- messages: |
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- role: user |
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content: What is your favorite condiment? |
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license: other |
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--- |
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# Usage |
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This model uses the 4-bits quantization. So you need to install bitsandbytes to use it. |
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```python |
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pip install bitsandbytes |
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``` |
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For inference (streaming): |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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import torch |
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from transformers import TextIteratorStreamer |
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from threading import Thread |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model_id = "Arthur-LAGACHERIE/Reflection-Gemma-2-2b" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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prompt = """ |
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### System |
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You are a world-class AI system, capable of complex reasoning and reflection. |
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Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. |
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If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags. |
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Try an answer and see if it's correct before generate the ouput. |
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But don't forget to think very carefully. |
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### Question |
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The question here. |
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""" |
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chat = [ |
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{ "role": "user", "content": prompt}, |
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] |
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question = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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question = tokenizer(question, return_tensors="pt").to(device) |
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True) |
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generation_kwargs = dict(question, streamer=streamer, max_new_tokens=4000) |
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thread = Thread(target=model.generate, kwargs=generation_kwargs) |
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# generate |
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thread.start() |
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for new_text in streamer: |
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print(new_text, end="") |
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``` |
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# Some info |
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If you want to know how I fine tune it, what datasets I used and the training code. [See here]() |
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# Model Trained Using AutoTrain |
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This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). |
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