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MaziyarPanahi
/
calme-2.4-rys-78b

Text Generation
Transformers
Safetensors
English
qwen2
chat
qwen
finetune
chatml
conversational
Eval Results
text-generation-inference
Model card Files Community
10
calme-2.4-rys-78b
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---
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
---

<img src="./calme-2.webp" alt="Calme-2 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

# 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)

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


```python

# 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.