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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text-generation |
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inference: false |
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tags: |
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- transformers |
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- gguf |
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- imatrix |
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- Saul-Instruct-v1 |
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--- |
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Quantizations of https://huggingface.co/Equall/Saul-Instruct-v1 |
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Note: not sure why but Q2_K, Q3_K_S, Q4_0 and Q5_0 gave error during quantizations: "ggml_validate_row_data: found nan value at block xxx", so I skipped those quants. |
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# From original readme |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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You can use it for legal use cases that involves generation. |
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Here's how you can run the model using the pipeline() function from 🤗 Transformers: |
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```python |
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# Install transformers from source - only needed for versions <= v4.34 |
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# pip install git+https://github.com/huggingface/transformers.git |
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# pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="Equall/Saul-Instruct-v1", torch_dtype=torch.bfloat16, device_map="auto") |
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# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{"role": "user", "content": "[YOUR QUERY GOES HERE]"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=False) |
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print(outputs[0]["generated_text"]) |
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``` |
