metadata

license: other
language:
  - en
pipeline_tag: text-generation
inference: false
tags:
  - transformers
  - gguf
  - imatrix
  - Saul-Instruct-v1

Quantizations of https://huggingface.co/Equall/Saul-Instruct-v1

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.

From original readme

Uses

You can use it for legal use cases that involves generation.

Here's how you can run the model using the pipeline() function from 🤗 Transformers:


# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="Equall/Saul-Instruct-v1", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "user", "content": "[YOUR QUERY GOES HERE]"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
print(outputs[0]["generated_text"])