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nagayama0706
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Model Details
Model Provider
nagayama0706
Model Tree
dalle-mini/dalle-mini
TheBloke/openchat_3.5-GPTQ
Input Modalities
Output Modalities
Supported Functionality
yaml
slices: - sources: - model: TheBloke/openchat_3.5-GPTQ layer_range: [0, 32] - model: dalle-mini/dalle-mini layer_range: [0, 32]merge_method: slerpbase_model: TheBloke/openchat_3.5-GPTQparameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5dtype: bfloat16python
!pip install -qU transformers accelerate from transformers import AutoTokenizerimport transformersimport torch model = "nagayama0706/video_generation_model"messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model)prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto",) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)print(outputs[0]["generated_text"])!pip install -qU transformers accelerate from transformers import AutoTokenizerimport transformersimport torch model = "nagayama0706/video_generation_model"messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model)prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto",) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)print(outputs[0]["generated_text"])