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RichardErkhov/
itsnebulalol_-_Llama-3.2-Nemotron-3B-gguf

Name: itsnebulalol_-_Llama-3.2-Nemotron-3B-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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Llama-3.2-Nemotron-3B - GGUF

Model creator: https://huggingface.co/itsnebulalol/
Original model: https://huggingface.co/itsnebulalol/Llama-3.2-Nemotron-3B/

Name	Quant method	Size
Llama-3.2-Nemotron-3B.Q2_K.gguf	Q2_K	1.27GB
Llama-3.2-Nemotron-3B.Q3_K_S.gguf	Q3_K_S	1.44GB
Llama-3.2-Nemotron-3B.Q3_K.gguf	Q3_K	1.57GB
Llama-3.2-Nemotron-3B.Q3_K_M.gguf	Q3_K_M	1.57GB
Llama-3.2-Nemotron-3B.Q3_K_L.gguf	Q3_K_L	1.69GB
Llama-3.2-Nemotron-3B.IQ4_XS.gguf	IQ4_XS	1.71GB
Llama-3.2-Nemotron-3B.Q4_0.gguf	Q4_0	1.79GB
Llama-3.2-Nemotron-3B.IQ4_NL.gguf	IQ4_NL	1.79GB
Llama-3.2-Nemotron-3B.Q4_K_S.gguf	Q4_K_S	1.8GB
Llama-3.2-Nemotron-3B.Q4_K.gguf	Q4_K	1.88GB
Llama-3.2-Nemotron-3B.Q4_K_M.gguf	Q4_K_M	1.88GB
Llama-3.2-Nemotron-3B.Q4_1.gguf	Q4_1	1.95GB
Llama-3.2-Nemotron-3B.Q5_0.gguf	Q5_0	2.11GB
Llama-3.2-Nemotron-3B.Q5_K_S.gguf	Q5_K_S	2.11GB
Llama-3.2-Nemotron-3B.Q5_K.gguf	Q5_K	2.16GB
Llama-3.2-Nemotron-3B.Q5_K_M.gguf	Q5_K_M	2.16GB
Llama-3.2-Nemotron-3B.Q5_1.gguf	Q5_1	2.28GB
Llama-3.2-Nemotron-3B.Q6_K.gguf	Q6_K	2.46GB
Llama-3.2-Nemotron-3B.Q8_0.gguf	Q8_0	3.19GB

Original model description:

license: llama3.2 language:

en inference: false fine-tuning: false tags:
nvidia
llama3.2 datasets:
nvidia/HelpSteer2 base_model: unsloth/Llama-3.2-3B-bnb-4bit pipeline_tag: text-generation library_name: transformers

🍷 Llama-3.2-Nemotron-3B

This is a finetune of meta-llama/Llama-3.2-3B (specifically, unsloth/Llama-3.2-3B-bnb-4bit).

It was trained on the nvidia/HelpSteer2 dataset, similar to nvidia/Llama-3.1-Nemotron-70B-Instruct-HF, using Unsloth.

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "itsnebulalol/Llama-3.2-Nemotron-3B"
messages = [{"role": "user", "content": "How many r in strawberry?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

RichardErkhov/itsnebulalol_-_Llama-3.2-Nemotron-3B-gguf

Original model description:

🍷 Llama-3.2-Nemotron-3B

💻 Usage

RichardErkhov/
itsnebulalol_-_Llama-3.2-Nemotron-3B-gguf