Llama-3.3-70B-Instruct-quantized.w4a16 👁 Model Icon

Model Overview

Model Architecture: Meta-Llama-3.1
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: INT4
Intended Use Cases: Intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases.
Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Release Date: 12/11/2024
Version: 1.0
Validated on: RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5
License(s): llama3.3
Model Developers: Red Hat (Neural Magic)

Model Optimizations

This model was obtained by quantizing the weights of Llama-3.3-70B-Instruct to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library.

Deployment

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16"
number_gpus = 1

sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
 {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
 {"role": "user", "content": "Who are you?"},
]

prompts = tokenizer.apply_chat_template(messages, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

Evaluation

This model was evaluated on the well-known OpenLLM v1, HumanEval, and HumanEval+ benchmarks. In all cases, model outputs were generated with the vLLM engine.

OpenLLM v1 evaluations were conducted using lm-evaluation-harness and the prompting style of Meta-Llama-3.1-Instruct-evals when available.

HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the EvalPlus repository.

Accuracy

Category	Benchmark	Llama-3.3-70B-Instruct	Llama-3.3-70B-Instruct-quantized.w4a16 (this model)	Recovery
OpenLLM v1	MMLU (5-shot)	81.60	80.62	98.8%
	MMLU (CoT, 0-shot)	86.58	85.81	99.1%
	ARC Challenge (0-shot)	49.23	49.49	100.5%
	GSM-8K (CoT, 8-shot, strict-match)	94.16	94.47	100.3%
	Hellaswag (10-shot)	86.49	85.97	99.4%
	Winogrande (5-shot)	84.77	84.45	99.6%
	TruthfulQA (0-shot, mc2)	62.75	61.66	98.3%
	Average	77.94	77.49	98.3%
Coding	HumanEval pass@1	83.20	83.40	100.2%
Coding	HumanEval+ pass@1	78.40	78.60	100.3%