Midashenglm 7b 1021 FP8

--- license: apache-2.0 language: - en - zh - th - id - vi pipeline_tag: audio-text-to-text tags: - multimodal - audio-language-model - audio base_model: - mispeech/dasheng-0.6B - Qwen/Qwen2.5-Omni-7B base_model_relation: finetune --- # MiDashengLM-7B-1021 (FP8) The FP8 weights for [mispeech/midashenglm-7b-1021-fp32](https://huggingface.co/mispeech/midashenglm-7b-1021-fp32). Optimized for Hopper-class (H100 and newer) GPUs, leveraging hardware support for enhanced performance and memory savings. While older GPUs may see limited performance gains, FP8 can still be used to conserve VRAM, and storage. ## Usage ### Load Model ```python from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer model_id = "mispeech/midashenglm-7b-1021-fp8" model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) ``` ### Construct Prompt ```python user_prompt = "Caption the audio." # You may try any other prompt messages = [ { "role": "user", "content": [ {"type": "text", "text": user_prompt}, { "type": "audio", "path": "/path/to/example.wav", # or "url": "https://example.com/example.wav" # or "audio": np.random.randn(16000) }, ], }, ] ``` ### Generate Output ```python import torch with torch.no_grad(): model_inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, add_special_tokens=True, return_dict=True, ).to(device=model.device, dtype=model.dtype) generation = model.generate(**model_inputs) output = tokenizer.batch_decode(generation, skip_special_tokens=True) # ["An engine is idling."] ``` ## Citation MiDashengLM is under the Apache License 2.0, and we encourage its use in **both research and business applications**. If you find MiDashengLM useful in your research, please consider citing our work: ```bibtex @techreport{midashenglm7b, title = {MiDashengLM: Efficient Audio Understanding with General Audio Captions}, author = {{Horizon Team, MiLM Plus}}, institution= {Xiaomi Inc.}, year = {2025}, note = {Contributors: Heinrich Dinkel et al. (listed alphabetically in Appendix B)}, url = {https://arxiv.org/abs/2508.03983}, eprint = {2508.03983}, } ```

Source context: 25 downloads · 5 likes · Pipeline audio-text-to-text · Repo mispeech/midashenglm-7b-1021-fp8

Source context: 16 downloads · 5 likes · Pipeline audio-text-to-text · Repo mispeech/midashenglm-7b-1021-fp8