by RedHatAI
Open source · 1M downloads · 4 likes
The Llama 3.2 1B Instruct FP8 dynamic model is an optimized and quantized version of the Llama 3.2 1B Instruct model, designed to deliver similar performance while significantly reducing memory and storage requirements. By quantizing both weights and activations to FP8, it halves the disk and GPU memory footprint while maintaining near-identical accuracy to the original. This model is particularly well-suited for English-language conversational assistance tasks, such as chatbots or automated response systems, with enhanced efficiency for deployment on constrained infrastructure. Its key advantages lie in its lightweight design and compatibility with tools like vLLM, while retaining reasoning and comprehension capabilities comparable to the unquantized model.
Quantized version of Llama-3.2-1B-Instruct. It achieves an average score of 50.88 on a subset of task from the OpenLLM benchmark (version 1), whereas the unquantized model achieves 51.70.
This model was obtained by quantizing the weights and activations of Llama-3.2-1B-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. LLM Compressor is used for quantization.
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-3.2-1B-Instruct-FP8-dynamic"
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)
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.
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 = "meta-llama/Llama-3.2-1B-Instruct"
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),
)
The model was evaluated on MMLU, ARC-Challenge, GSM-8K, and Winogrande. Evaluation was conducted using the Neural Magic fork of lm-evaluation-harness (branch llama_3.1_instruct) and the vLLM engine. This version of the lm-evaluation-harness includes versions of ARC-Challenge, GSM-8K, MMLU, and MMLU-cot that match the prompting style of Meta-Llama-3.1-Instruct-evals.
| Benchmark | Llama-3.2-1B-Instruct | Llama-3.2-1B-Instruct-FP8-dynamic (this model) | Recovery |
| MMLU (5-shot) | 47.66 | 47.55 | 99.8% |
| MMLU-cot (0-shot) | 47.10 | 46.79 | 99.3% |
| ARC Challenge (0-shot) | 58.36 | 57.25 | 98.1% |
| GSM-8K-cot (8-shot, strict-match) | 45.72 | 45.94 | 100.5% |
| Winogrande (5-shot) | 62.27 | 61.40 | 98.6% |
| Hellaswag (10-shot) | 61.01 | 60.95 | 99.9% |
| TruthfulQA (0-shot, mc2) | 43.52 | 44.23 | 101.6% |
| Average | 52.24 | 52.02 | 99.7% |
The results were obtained using the following commands:
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
--tasks mmlu_llama_3.1_instruct \
--fewshot_as_multiturn \
--apply_chat_template \
--num_fewshot 5 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \
--tasks mmlu_cot_0shot_llama_3.1_instruct \
--apply_chat_template \
--num_fewshot 0 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \
--tasks arc_challenge_llama_3.1_instruct \
--apply_chat_template \
--num_fewshot 0 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \
--tasks gsm8k_cot_llama_3.1_instruct \
--fewshot_as_multiturn \
--apply_chat_template \
--num_fewshot 8 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks hellaswag \
--num_fewshot 10 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks winogrande \
--num_fewshot 5 \
--batch_size auto
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks truthfulqa \
--num_fewshot 0 \
--batch_size auto