RichardErkhov/motexture_-_LlamaXCoder-3.2-3B-Instruct-awq

Quantization made by Richard Erkhov.

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LlamaXCoder-3.2-3B-Instruct - AWQ

• Model creator: https://huggingface.co/motexture/
• Original model: https://huggingface.co/motexture/LlamaXCoder-3.2-3B-Instruct/

Original model description:

license: apache-2.0 datasets: - motexture/cData language: - en - it - es base_model: - meta-llama/Llama-3.2-3B-Instruct pipeline_tag: text-generation tags: - coding - coder - model - llama

LlamaXCoder-3.2-3B-Instruct

Introduction

LlamaXCoder-3.2-3B-Instruct is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct, trained on the cData coding dataset to improve its reasoning and coding ability.

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/LlamaXCoder-3.2-3B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("motexture/LlamaXCoder-3.2-3B-Instruct")

prompt = "Write a C++ program that prints Hello World!"
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,
        do_sample=True,
        temperature=0.3
)
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]

License

Apache 2.0