Qwen3-14B-BaronLLM-v2-Q8

78

3

14.0B

Q8

llama-cpp

by

Trendyol

Other

OTHER

14B params

New

78 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

32GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

14GB+ RAM

Code Examples

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

🚀 Usage & Accesspythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

def generate(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))

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