Qwen3-8B-HoneyBadger-EXP

3

4

8.0B

1 language

license:apache-2.0

by

ZeroXClem

Language Model

OTHER

8B params

New

3 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

18GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

8GB+ RAM

Code Examples

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐍 Python Code Snippetpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Qwen3-8B-HoneyBadger-EXP"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a short story about a detective solving a paradox in time."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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