deepseek_v3_mini_50m

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Mostafa8Mehrabi

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Quick Summary

A compact version of DeepSeek-V3 Mini with exactly 58,283,136 parameters (reduced from ~181M).

Code Examples

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Model Specificationspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("./deepseek_v3_mini_50m")
tokenizer = AutoTokenizer.from_pretrained("./deepseek_v3_mini_50m")

# Quick test
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

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