This model is a fine-tuned version of the
dnhkng/RYS-XLarge
, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.
Use Cases
This model is suitable for a wide range of applications, including but not limited to:
# Use a pipeline as a high-level helperfrom transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-rys-78b")
pipe(messages)
# Load model directlyfrom transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-rys-78b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-rys-78b")
Ethical Considerations
As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.
Runs of MaziyarPanahi calme-2.1-rys-78b on huggingface.co
291
Total runs
0
24-hour runs
-23
3-day runs
-72
7-day runs
140
30-day runs
More Information About calme-2.1-rys-78b huggingface.co Model
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calme-2.1-rys-78b is an open source model from GitHub that offers a free installation service, and any user can find calme-2.1-rys-78b on GitHub to install. At the same time, huggingface.co provides the effect of calme-2.1-rys-78b install, users can directly use calme-2.1-rys-78b installed effect in huggingface.co for debugging and trial. It also supports api for free installation.