Instructions to use Vizzier/400m-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vizzier/400m-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vizzier/400m-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Vizzier/400m-Instruct") model = AutoModelForCausalLM.from_pretrained("Vizzier/400m-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Vizzier/400m-Instruct with 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
- SGLang
How to use Vizzier/400m-Instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Vizzier/400m-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Vizzier/400m-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vizzier/400m-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Vizzier/400m-Instruct with Docker Model Runner:
docker model run hf.co/Vizzier/400m-Instruct
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
α₯α«α?<|im_end|>
<|im_start|>assistant
For example:
<|im_start|>user
αΆα΅α΅ α¨α ααͺα« ααα«α΅ α₯αα΅αα<|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": "αΆα΅α΅ α¨α ααͺα« ααα«α΅ α₯αα΅αα"}]
llama3_am(messages, max_new_tokens=128, repetition_penalty=1.1, return_full_text=False)
Output:
[{'generated_text': '1. αα₯α
2. ααααͺα« 3. αα'}]
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rasyosef/Llama-3.2-400M-Amharic