metadata

language:
  - en
library_name: transformers
tags:
  - chat
  - qwen
  - qwen2
  - finetune
  - chatml
base_model: dnhkng/RYS-XLarge
datasets:
  - MaziyarPanahi/truthy-dpo-v0.1-axolotl
model_name: calme-2.4-rys-78b
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
license: mit

MaziyarPanahi/calme-2.4-rys-78b

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:

Advanced question-answering systems
Intelligent chatbots and virtual assistants
Content generation and summarization
Code generation and analysis
Complex problem-solving and decision support

⚡ Quantized GGUF

Coming soon!

🏆 Open LLM Leaderboard Evaluation Results

Coming soon!

Prompt Template

This model uses ChatML prompt template:

<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}

How to use


# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.4-rys-78b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.4-rys-78b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.4-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.