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Model: itsnebulalol/Llama-3.2-3B-Instruct-Alpaca
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---
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
datasets:
- yahma/alpaca-cleaned
---
# 🍷 Llama-3.2-3B-Instruct-Alpaca
This is a finetune of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
It was trained on the [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset using Unsloth.
This was my first fine tune and it's not done the best, but it is usable for small applications.
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "itsnebulalol/Llama-3.2-3B-Instruct-Alpaca"
messages = [{"role": "user", "content": "What is a large language model?"}]
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)

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"hidden_act": "silu",
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"rope_type": "llama3"
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"rope_theta": 500000.0,
"tie_word_embeddings": true,
"torch_dtype": "float16",
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"vocab_size": 128256
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