Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1

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10B params
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23GB+ RAM
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Quick Summary

- bits: 4 - dynamic: null - groupsize: 32 - descact: true - staticgroups: false - sym: true - lmhead: false - truesequential: true - quantmethod: "gptq" - check...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
10GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1 with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 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

Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
Example:pythontransformers
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)

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