How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vizzier/400m-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vizzier/400m-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/Vizzier/400m-Instruct
Quick Links

This model is an Instruction-Tuned version of Llama 3.2 400M Amharic.

How to use

Chat Format

Given the nature of the training data, the phi-2 instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follows:

<|im_start|>user
αŒ₯ያቄ?<|im_end|>
<|im_start|>assistant

For example:

<|im_start|>user
αˆΆαˆ΅α‰΅ α‹¨αŠ ααˆͺካ αˆ€αŒˆαˆ«α‰΅ αŒ₯α‰€αˆ΅αˆαŠ<|im_end|>
<|im_start|>assistant

where the model generates the text after <|im_start|>assistant .

Sample inference code

First, you need to install the latest version of transformers

pip install -Uq transformers

You can use this model directly with a pipeline for text generation:

from transformers import pipeline

llama3_am = pipeline(
    "text-generation",
    model="rasyosef/Llama-3.2-400M-Amharic-Instruct",
    device_map="auto"
  )

messages = [{"role": "user", "content": "αˆΆαˆ΅α‰΅ α‹¨αŠ ααˆͺካ αˆ€αŒˆαˆ«α‰΅ αŒ₯α‰€αˆ΅αˆαŠ"}]
llama3_am(messages, max_new_tokens=128, repetition_penalty=1.1, return_full_text=False)

Output:

[{'generated_text': '1. ግα‰₯ፅ 2. αŠ“α‹­αŒ„αˆͺα‹« 3. αŒ‹αŠ“'}]
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