metadata

language:
  - en
  - de
  - fr
  - it
  - pt
  - hi
  - es
  - th
library_name: transformers
pipeline_tag: text-generation
tags:
  - facebook
  - meta
  - pytorch
  - llama
  - llama-3
license: llama3.2
base_model:
  - meta-llama/Llama-3.2-1B-Instruct
datasets:
  - motexture/iData

iTech-1B-Instruct

Introduction

iTech-1B-Instruct is a fine-tuned version of Llama-3.2.1B-Instruct, trained on the iData dataset.

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "motexture/iTech-1B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("motexture/iTech-1B-Instruct")

prompt = "Write a C++ program that demonstrates the concept of separate compilation and linkage using namespaces and header files. The program should consist of multiple source files, each containing a portion of the program's code, and a header file that contains the interface information for the program.\n\nThe program should define a namespace my_namespace that contains a class  MyClass with a member function print() that takes an integer as an argument. The program should also define a function main() that uses an object of the MyClass class to print a message.\n\nThe program should be compiled and linked separately, with each source file being compiled individually and then linked together to form the final executable."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Responsibility & Safety

As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:

Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
Provide protections for the community to help prevent the misuse of our models