library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- int8
- vllm
base_model: HuggingFaceTB/SmolLM-1.7B-Instruct
SmolLM-1.7B-Instruct-quantized.w8a16
Model Overview
- Model Architecture: Llama
- Input: Text
- Output: Text
- Model Optimizations:
- Weight quantization: INT8
- Intended Use Cases: Intended for commercial and research use in English. Similarly to SmolLM-1.7B-Instruct, this models 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: 8/16/2024
- Version: 1.0
- License(s): Apache-2.0
- Model Developers: Neural Magic
Quantized version of SmolLM-1.7B-Instruct. It achieves an average score of 41.83 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 41.76.
Model Optimizations
This model was obtained by quantizing the weights of SmolLM-1.7B-Instruct to INT8 data type. 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 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 INT8 and floating point representations of the quantized weights. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. GPTQ used a 1% damping factor and 1,024 sequences of 2,048 random tokens.
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/SmolLM-1.7B-Instruct-quantized.w8a16"
sampling_params = SamplingParams(temperature=0.6, top_p=0.92, max_tokens=100)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "user", "content": "List the steps to bake a chocolate cake from scratch."},
]
prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
llm = LLM(model=model_id)
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.
Creation
This model was created by using the llm-compressor library as presented in the code snipet below.
from transformers import AutoTokenizer
from datasets import Dataset
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
import random
model_id = "HuggingFaceTB/SmolLM-1.7B-Instruct"
num_samples = 1024
max_seq_len = 2048
tokenizer = AutoTokenizer.from_pretrained(model_id)
max_token_id = len(tokenizer.get_vocab()) - 1
input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
attention_mask = num_samples * [max_seq_len * [1]]
ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
recipe = GPTQModifier(
targets="Linear",
scheme="W8A16",
ignore=["lm_head"],
dampening_frac=0.01,
)
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("SmolLM-1.7B-Instruct-quantized.w8a16")
Evaluation
The model was evaluated on the OpenLLM leaderboard tasks (version 1) with the lm-evaluation-harness (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the vLLM engine, using the following command:
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/SmolLM-1.7B-Instruct-quantized.w8a16",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \
--tasks openllm \
--batch_size auto
Accuracy
Open LLM Leaderboard evaluation scores
| Benchmark | SmolLM-1.7B-Instruct-quantized | SmolLM-1.7B-Instruct-quantized.w8a16 (this model) | Recovery |
| MMLU (5-shot) | 28.10 | 28.42 | 101.1% |
| ARC Challenge (25-shot) | 49.06 | 49.32 | 100.5% |
| GSM-8K (5-shot, strict-match) | 4.93 | 4.93 | 100.0% |
| Hellaswag (10-shot) | 66.96 | 66.89 | 99.9% |
| Winogrande (5-shot) | 61.01 | 61.17 | 100.3% |
| TruthfulQA (0-shot) | 40.28 | 40.25 | 99.4% |
| Average | 41.76 | 41.83 | 100.2% |