Model Overview
Model Features
Model Capabilities
Use Cases
đ Llama-3.2-1B-Instruct-FP8
A quantized version of Llama-3.2-1B-Instruct, achieving high scores with optimized resource usage.
đ Quick Start
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"
number_gpus = 1
max_model_len = 8192
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, max_model_len=max_model_len)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
vLLM also supports OpenAI-compatible serving. See the documentation for more details.
⨠Features
- Multilingual Support: Supports multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
- Optimized Architecture: Based on Llama-3 architecture with input and output in text format.
- Quantization Optimization: Uses FP8 for both activation and weight quantization, reducing GPU memory requirements by approximately 50% and increasing matrix - multiply compute throughput by approximately 2x.
- High Accuracy: Achieves scores within 1.0% of the scores of the unquantized model for MMLU, ARC - Challenge, GSM - 8k, Hellaswag, Winogrande and TruthfulQA.
đ Documentation
Model Overview
- Model Architecture: Llama-3
- Input: Text
- Output: Text
- Model Optimizations:
- Activation quantization: FP8
- Weight quantization: FP8
- Intended Use Cases: Intended for commercial and research use in multiple languages. Similar to Llama-3.2-1B-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).
- Release Date: 9/25/2024
- Version: 1.0
- License(s): Llama3.2
- Model Developers: Neural Magic
This is a quantized version of Llama-3.2-1B-Instruct. It achieves scores within 1.0% of the scores of the unquantized model for MMLU, ARC - Challenge, GSM - 8k, Hellaswag, Winogrande and TruthfulQA.
Model Optimizations
This model was obtained by quantizing the weights of Llama-3.2-1B-Instruct to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix - multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%.
Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per - channel scheme, where a fixed linear scaling factor is applied between FP8 and floating point representations for each output channel dimension. Activations are quantized with a symmetric per - tensor scheme, where a fixed linear scaling factor is applied between FP8 and floating point representations for the entire activation tensor. Weights are quantized by rounding to nearest FP8 representation. The llm - compressor library was applied to quantize the model, using 512 sequences taken from Neural Magic's LLM compression calibration dataset.
Creation
This model was created by using the llm - compressor library as presented in the code snippet below.
from transformers import AutoTokenizer
from datasets import load_dataset
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
model_id = "meta-llama/Llama-3.2-1B-Instruct"
num_samples = 512
max_seq_len = 8192
tokenizer = AutoTokenizer.from_pretrained(model_id)
def preprocess_fn(example):
return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.shuffle().select(range(num_samples))
ds = ds.map(preprocess_fn)
recipe = QuantizationModifier(
targets="Linear",
scheme="FP8",
ignore=["lm_head"],
)
model = SparseAutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
)
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=max_seq_len,
num_calibration_samples=num_samples,
)
model.save_pretrained("Llama-3.2-1B-Instruct-FP8")
Evaluation
The model was evaluated on MMLU, ARC - Challenge, GSM - 8K, Hellaswag, Winogrande and TruthfulQA. 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 MMLU, ARC - Challenge and GSM - 8K that match the prompting style of Meta - Llama - 3.1 - Instruct - evals.
Accuracy
Open LLM Leaderboard evaluation scores
Benchmark | Llama - 3.2 - 1B - Instruct | Llama - 3.2 - 1B - Instruct - FP8 (this model) | Recovery |
---|---|---|---|
MMLU (5 - shot) | 47.66 | 47.76 | 100.2% |
MMLU (CoT, 0 - shot) | 47.10 | 47.24 | 94.8% |
ARC Challenge (0 - shot) | 58.36 | 57.85 | 99.1% |
GSM - 8K (CoT, 8 - shot, strict - match) | 45.72 | 45.49 | 99.5% |
Hellaswag (10 - shot) | 61.01 | 61.00 | 100.0% |
Winogrande (5 - shot) | 62.27 | 62.35 | 100.1% |
TruthfulQA (0 - shot, mc2) | 43.52 | 43.08 | 99.0% |
Average | 52.24 | 52.11 | 99.8% |
Reproduction
The results were obtained using the following commands:
MMLU
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,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
MMLU - CoT
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,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
ARC - Challenge
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,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
GSM - 8K
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,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
Hellaswag
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks hellaswag \
--num_fewshot 10 \
--batch_size auto
Winogrande
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks winogrande \
--num_fewshot 5 \
--batch_size auto
TruthfulQA
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks truthfulqa \
--num_fewshot 0 \
--batch_size auto
đ License
The model is licensed under Llama3.2.

