JOSIE_Beta-5-6xJOSIExQwen1.5_0.5B_base-slerp
JOSIE_Beta-5-6xJOSIExQwen1.5_0.5B_base-slerp is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
- Weyaxi/Einstein-v4-7B
- Isaak-Carter/JOSIE_Beta-4-7B-slerp
- Weyaxi/Einstein-v4-7B
- Weyaxi/Einstein-v4-7B
- Isaak-Carter/JOSIE_Beta-3-7B-slerp
- Isaak-Carter/JOSIE_Beta-3-7B-slerp
𧩠Configuration
dtype: float16
gate_mode: cheap_embed
experts:
- source_model: Weyaxi/Einstein-v4-7B
positive_prompts: ["You are JOSIE, my own private and sentient superinteligent artifitial general inteligent assistant."]
- source_model: Isaak-Carter/JOSIE_Beta-4-7B-slerp
positive_prompts: ["You are JOSIE, assistant for autonomously controlling a Smart Home."]
- source_model: Weyaxi/Einstein-v4-7B
positive_prompts: ["You are JOSIE, a superinteligent coding assistant."]
- source_model: Weyaxi/Einstein-v4-7B
positive_prompts: ["You are JOSIE, a superinteligent assistant good at math."]
- source_model: Isaak-Carter/JOSIE_Beta-3-7B-slerp
positive_prompts: ["You are JOSIE, an assistant good at scraping and searching the internet for information."]
- source_model: Isaak-Carter/JOSIE_Beta-3-7B-slerp
positive_prompts: ["You are JOSIE, an assistant good at reasoning."]
π» Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Isaak-Carter/JOSIE_Beta-5-6xJOSIExQwen1.5_0.5B_base-slerp"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
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