aquif-3-moe-17B-A2.8B

20
14
17.0B
1 language
license:apache-2.0
by
aquif-ai
Language Model
OTHER
17B params
New
20 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
38GB+ RAM
Mobile
Laptop
Server
Quick Summary

A high-performance mixture-of-experts language model optimized for efficiency, coding, science, and general use.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
16GB+ RAM

Code Examples

Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquif/aquif-3-moe-17b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Explain quantum entanglement:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

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