Qwen2.5-7B-HomerFuse-NerdExp

2

3

7.0B

1 language

license:apache-2.0

by

ZeroXClem

Language Model

OTHER

7B params

New

2 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

16GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

7GB+ RAM

Code Examples

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🤗 Hugging Face Transformers (Python)pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

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