--- language: - en license: apache-2.0 tags: - Mistral-Small - instruct - finetune - chatml - gpt4 - synthetic data - distillation - function calling - json mode - axolotl - roleplaying - chat - reasoning - r1 - vllm - fp8 base_model: NousResearch/DeepHermes-3-Mistral-24B-Preview widget: - example_title: DeepHermes 3 messages: - role: system content: >- You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. - role: user content: What is the meaning of life? model-index: - name: DeepHermes-3-Mistral-24B-Preview-FP8-Dynamic results: [] library_name: transformers --- # Model Overview - **Model Architecture:** Mistral-Small-24B-Instruct-2501 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 3/6/2025 - **Version:** 1.0 Quantized version of [DeepHermes-3-Mistral-24B-Preview](https://huggingface.co/NousResearch/DeepHermes-3-Mistral-24B-Preview) which is based on [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501). ### Model Optimizations This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. ## Deployment ### Use with vLLM This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams max_model_len, tp_size = 4096, 1 model_name = "th-nuernberg/DeepHermes-3-Mistral-24B-Preview-FP8-Dynamic" tokenizer = AutoTokenizer.from_pretrained(model_name) llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True) sampling_params = SamplingParams(temperature=0.15, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) messages_list = [ [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}], ] prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) generated_text = [output.outputs[0].text for output in outputs] print(generated_text) ``` vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. ```bash vllm serve "th-nuernberg/DeepHermes-3-Mistral-24B-Preview-FP8-Dynamic" \ --served-model-name 'th-nuernberg/DeepHermes-3-Mistral-24B-Preview' \ --trust-remote-code \ --enable-reasoning --reasoning-parser deepseek_r1 \ --max-model-len 32768 --quantization compressed-tensors ``` ## Creation This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. ```python import argparse from transformers import AutoModelForCausalLM, AutoTokenizer from llmcompressor.modifiers.quantization import QuantizationModifier from llmcompressor.transformers import oneshot import os def main(): parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8') parser.add_argument('--model_id', type=str, required=True, help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")') parser.add_argument('--save_path', type=str, default='.', help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic') args = parser.parse_args() # Load model model = AutoModelForCausalLM.from_pretrained( args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(args.model_id) # Configure the quantization algorithm and scheme recipe = QuantizationModifier( targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"] ) # Apply quantization oneshot(model=model, recipe=recipe) save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic") os.makedirs(save_path, exist_ok=True) # Save to disk in compressed-tensors format model.save_pretrained(save_path) tokenizer.save_pretrained(save_path) print(f"Model and tokenizer saved to: {save_path}") if __name__ == "__main__": main() ```