[!TIP] This is an experimental model, so it might not perform well for some prompts and may be sensitive to hyper parameters. I would appreciate any feedback to see if I can fix any issues in the next iteration. ❤️
MaziyarPanahi/calme-3.1-instruct-78b
This model is an advanced iteration of the powerful Qwen/Qwen2.5-72B, specifically fine-tuned to enhance its capabilities in generic domains. The Qwen2.5-72B base model was merged with itself to create a larger model. After that, the model was fine-tuned on a custom datasets.
⚡ Quantized GGUF
Thanks to mradermacher: calme-3.1-instruct-78b-GGUF
🏆 Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 51.20 |
| IFEval (0-Shot) | 81.36 |
| BBH (3-Shot) | 62.41 |
| MATH Lvl 5 (4-Shot) | 38.75 |
| GPQA (0-shot) | 19.46 |
| MuSR (0-shot) | 36.50 |
| MMLU-PRO (5-shot) | 68.72 |
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-3.1-instruct-78b")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.1-instruct-78b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.1-instruct-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.