Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic

Model Overview

Model Architecture: Llama-3.1-Nemotron
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
Intended Use Cases: Intended for commercial and research use in multiple languages. Similarly to Llama-3.1-Nemotron-70B-Instruct, this model is intended for assistant-like chat.
Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
Release Date: 10/17/2024
Version: 1.0
License(s): llama3.1
Model Developers: Neural Magic

This model is a quantized version of Llama-3.1-Nemotron-70B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic achieves 99.41% recovery for the Arena-Hard evaluation, 100% for OpenLLM v1 (using Meta's prompting when available), and ToDo for OpenLLM v2.

Model Optimizations

This model was obtained by quantizing the weights and activations of Llama-3.1-Nemotron-70B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis.

Deployment

Use with vLLM

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 = "neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic"
number_gpus = 2

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, add_generation_prompt=True, 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

This model was created by applying LLM-Compressor, as presented in the code snipet below.

import torch

from transformers import AutoTokenizer

from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.transformers.compression.helpers import (  # noqa
    calculate_offload_device_map,
    custom_offload_device_map,
)

recipe = """
quant_stage:
    quant_modifiers:
        QuantizationModifier:
            ignore: ["lm_head"]
            config_groups:
                group_0:
                    weights:
                        num_bits: 8
                        type: float
                        strategy: channel
                        dynamic: false
                        symmetric: true
                    input_activations:
                        num_bits: 8
                        type: float
                        strategy: token
                        dynamic: true
                        symmetric: true
                    targets: ["Linear"]
"""

model_stub = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
model_name = model_stub.split("/")[-1]

device_map = calculate_offload_device_map(
    model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype="auto"
)

model = SparseAutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map=device_map
)

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
    output_dir=output_dir,
    save_compressed=True,
    tokenizer=AutoTokenizer.from_pretrained(model_stub),
)

Evaluation

This model was evaluated on the well-known Arena-Hard, OpenLLM v1, and OpenLLM v2. In all cases, model outputs were generated with the vLLM engine.

Arena-Hard evaluations were conducted using the Arena-Hard-Auto repository.

OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of lm-evaluation-harness (branch llama_3.1_instruct). This version of the lm-evaluation-harness includes versions of MMLU, ARC-Challenge and GSM-8K that match the prompting style of Meta-Llama-3.1-Instruct-evals and a few fixes to OpenLLM v2 tasks.

Accuracy

Benchmark	nvidia/Llama-3.1-Nemotron-70B-Instruct-HF	neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic (this model)	Recovery
Arena Hard	85.0	84.5	99.41%
OpenLLM Leaderboard v1	80.1	80.3	100.2%
OpenLLM Leaderboard v2	40.2	39.8	99.0%

Benchmark (per-task breakdown)	nvidia/Llama-3.1-Nemotron-70B-Instruct-HF	neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic (this model)	Recovery
OpenLLM v1
MMLU (5-shot)	83.51	83.49	99.97%
MMLU-cot (0-shot)	85.89	86.18	100.33%
ARC Challenge (0-shot)	93.09	93.09	100%
GSM-8K-cot (8-shot, strict-match)	70.13	69.98	99.78%
Hellaswag (10-shot)	87.39	87.22	99.80%
Winogrande (5-shot)	84.93	84.93	100%
TruthfulQA (0-shot, mc2)	55.97	57.12	102.05%
Average	80.1	80.3	100.2%
OpenLLM v2
MMLU-Pro (5-shot)	43.45	42.99	98.94%
IFEval (0-shot)	73.32	74.08	101.02%
BBH (3-shot)	47.12	46.88	99.5%
Math-lvl-5 (4-shot)	23.85	21.78	91.32%
MuSR (0-shot)	13.5	13.35	98.88%
Average	40.2	39.8	99%

Reproduction

The results were obtained using the following commands:

MMLU

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size auto

MMLU-cot

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks mmlu_cot_0shot_llama_3.1_instruct \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto

ARC-Challenge

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks arc_challenge_llama_3.1_instruct \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto

GSM-8K

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks gsm8k_cot_llama_3.1_instruct \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --num_fewshot 8 \
  --batch_size auto

Hellaswag

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks hellaswag \
  --num_fewshot 10 \
  --batch_size auto

Winogrande

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks winogrande \
  --num_fewshot 5 \
  --batch_size auto

TruthfulQA

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks truthfulqa \
  --num_fewshot 0 \
  --batch_size auto

OpenLLM v2

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --batch_size auto

neuralmagic
/

Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic