Instructions to use QuietImpostor/Llama-3.1-Mini-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuietImpostor/Llama-3.1-Mini-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuietImpostor/Llama-3.1-Mini-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuietImpostor/Llama-3.1-Mini-Instruct")
model = AutoModelForCausalLM.from_pretrained("QuietImpostor/Llama-3.1-Mini-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 QuietImpostor/Llama-3.1-Mini-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuietImpostor/Llama-3.1-Mini-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": "QuietImpostor/Llama-3.1-Mini-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuietImpostor/Llama-3.1-Mini-Instruct

SGLang

How to use QuietImpostor/Llama-3.1-Mini-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 "QuietImpostor/Llama-3.1-Mini-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": "QuietImpostor/Llama-3.1-Mini-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 "QuietImpostor/Llama-3.1-Mini-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": "QuietImpostor/Llama-3.1-Mini-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use QuietImpostor/Llama-3.1-Mini-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuietImpostor/Llama-3.1-Mini-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuietImpostor/Llama-3.1-Mini-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuietImpostor/Llama-3.1-Mini-Instruct to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="QuietImpostor/Llama-3.1-Mini-Instruct",
    max_seq_length=2048,
)

Docker Model Runner
How to use QuietImpostor/Llama-3.1-Mini-Instruct with Docker Model Runner:
```
docker model run hf.co/QuietImpostor/Llama-3.1-Mini-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama 3.1 Mini - LoRA Finetuned

Model Description

This model is a LoRA-finetuned version of the Llama 3.1 Mini model, which is a pruned variant of the Llama 3.1 8B model. The original Llama 3.1 Mini was created by pruning the larger model to approximately 3 billion parameters, and this version has been further adapted using Low-Rank Adaptation (LoRA) to enhance its capabilities.

Limitations

Please note that this model, like its base version, may exhibit biases present in its training data and should be used with appropriate care and consideration.

Training Data

The base model (Llama 3.1 Mini) was trained on my personal Claude 3 Opus and Claude 3.5 Sonnet dataset, with some synthetic pairs added on with Gemma 2 9B it being the user, and Llama 3 70B through Groq being the assistant. I have also used Guanaco alongside everything else.

License

Llama 3.1

Downloads last month: 13

Safetensors

Model size

3B params

Tensor type

F16

Model tree for QuietImpostor/Llama-3.1-Mini-Instruct

Quantizations

1 model