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Chang-chih
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Model Details
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Chang-chih
Model Tree
deepseek-ai/deepseek-llm-7b-chat
Input Modalities
Output Modalities
Supported Functionality
leviathan-16B-final (旗艦參謀)deepseek-ai/deepseek-llm-7b-chatdistillation_dataset.jsonl (336 筆，由導師模型深度解析產出)python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "Chang-chih/leviathan-9B-MoE" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) prompt = "你是誰？"inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=50)response = tokenizer.decode(outputs[0], skip_special_tokens=True)print(response)使用 Ollamabashollama run hf.co/Chang-chih/leviathan-9B-MoE "你的指令"硬體需求 (Hardware Requirements)GPU VRAM：建議 16GB 以上 (可使用 4-bit 量化) 系統：Linux / Windows (透過 WSL) 授權 (License)本模型採用 Apache-2.0 授權條款。引用 (Citation)如果您使用此模型，請引用： bibtex@misc{leviathan-9b-moe, author = {Chang-chih}, title = {Leviathan-9B-MoE: A Mixture-of-Experts Model for Local Vision-Language Integration}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/Chang-chih/leviathan-9B-MoE}}from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "Chang-chih/leviathan-9B-MoE" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) prompt = "你是誰？"inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=50)response = tokenizer.decode(outputs[0], skip_special_tokens=True)print(response)使用 Ollamabashollama run hf.co/Chang-chih/leviathan-9B-MoE "你的指令"硬體需求 (Hardware Requirements)GPU VRAM：建議 16GB 以上 (可使用 4-bit 量化) 系統：Linux / Windows (透過 WSL) 授權 (License)本模型採用 Apache-2.0 授權條款。引用 (Citation)如果您使用此模型，請引用： bibtex@misc{leviathan-9b-moe, author = {Chang-chih}, title = {Leviathan-9B-MoE: A Mixture-of-Experts Model for Local Vision-Language Integration}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/Chang-chih/leviathan-9B-MoE}}