Nemotron-H-47B-Reasoning-128K

981

18

47.0B

1 language

—

by

nvidia

Language Model

OTHER

47B params

New

981 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

106GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

44GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Nemotron-H-47B-Reasoning-128K with quality assessment

Specialized For

general

multilingual

Training Datasets (1)

🔵 6/10

general

multilingual

Key Strengths

•Scale and Accessibility: 750GB of publicly available, filtered text
•Systematic Filtering: Documented heuristics enable reproducibility
•Language Diversity: Despite English-only, captures diverse writing styles

Considerations

•English-Only: Limits multilingual applications
•Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

**Use it with Transformers**texttransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-47B-Reasoning-128K")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/Nemotron-H-47B-Reasoning-128K",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

texttransformers

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

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