Hermes-2-Pro-Mistral-7B-infinity

Hermes 2 Pro Mistral-7B Infinity-Instruct

This model is a fine-tuned version of NousResearch/Hermes-2-Pro-Mistral-7B on the BAAI/Infinity-Instruct dataset.

Model Details

• Base Model: NousResearch/Hermes-2-Pro-Mistral-7B
• Dataset: BAAI/Infinity-Instruct
• Sequence Length: 8192 tokens
• Training:
• Epochs: 1
• Hardware: 4 Nodes x 4 NVIDIA A100 40GB GPUs
• Duration: 26:56:43
• Cluster: KIT SCC Cluster

Benchmark n_shots=0

For detailed benchmark results, including sub-categories and various metrics, please refer to the full benchmark table at the end of this README.

GGUF and Quantizations

• llama.cpp b3166
• Hermes-2-Pro-Mistral-7B-infinity-GGUF is availabe in:
• F16 Q8_0 Q6_KQ5_K_M Q4_K_M Q3_K_M Q2_K

Usage

To use this model, you can load it using the Hugging Face Transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-model-name")
tokenizer = AutoTokenizer.from_pretrained("your-model-name")

# Generate text
input_text = "Your prompt here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

License

This model is released under the Apache 2.0 license.

Acknowledgements

Special thanks to:

• NousResearch for their excellent base model
• BAAI for providing the Infinity-Instruct dataset

Citation

If you use this model in your research, consider citing. Although definetly cite NousResearch and BAAI:

@misc{hermes2pro-mistral-7b-infinity,
  author = {juvi21},
  title = {Hermes 2 Pro Mistral-7B Infinity-Instruct},
  year = {2024},
}

full-benchmark-results