itsnebulalol
/

Llama-3.2-Nemotron-3B-Instruct

@@ -1,202 +1,52 @@
 ---
-library_name: transformers
 tags:
-- unsloth
-- trl
-- sft
 ---
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 ---
+license: llama3.2
+language:
+- en
+inference: false
+fine-tuning: false
 tags:
+- nvidia
+- llama3.2
+datasets:
+- nvidia/HelpSteer2
+base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
+pipeline_tag: text-generation
+library_name: transformers
 ---
+# 🍷 Llama-3.2-Nemotron-3B-Instruct
+This is a finetune of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) (specifically, [unsloth/Llama-3.2-3B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-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-Instruct"
+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)