from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("QuantLLM/Meta-Llama-3-70B-Instruct-4bit-gguf")
tokenizer = AutoTokenizer.from_pretrained("QuantLLM/Meta-Llama-3-70B-Instruct-4bit-gguf")

# Generate text
inputs = tokenizer("Once upon a time", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With QuantLLM

python

from quantllm import TurboModel

# Load with automatic optimization
model = TurboModel.from_pretrained("QuantLLM/Meta-Llama-3-70B-Instruct-4bit-gguf")

# Generate
response = model.generate("Write a poem about coding")
print(response)

Requirements

pip install transformers torch

📊 Model Details

Property	Value
Original Model	meta-llama/Meta-Llama-3-70B-Instruct
Format	GUFF
Quantization	Full Precision
License	`apache-2.0`
Export Date	2026-04-24
Exported By	QuantLLM v2.0

🚀 Created with QuantLLM

Convert any model to GGUF, ONNX, or MLX in one line!

python

from quantllm import turbo

# Load any HuggingFace model
model = turbo("meta-llama/Meta-Llama-3-70B-Instruct")

# Export to any format
model.export("guff", quantization="Q4_K_M")

# Push to HuggingFace
model.push("your-repo", format="guff")

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