---
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](https://huggingface.co/meta-llama/Llama-3.2-3B) (specifically, [unsloth/Llama-3.2-3B-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-3B-bnb-4bit)).

It was trained on the [nvidia/HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) dataset, similar to [nvidia/Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/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](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)