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Model: nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# SmolLM-135M-Instruct-quantized.w4a16
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## Model Overview
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- **Model Architecture:** SmolLM-135M-Instruct
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** INT4
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- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M), this models is intended for assistant-like chat.
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
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- **Release Date:** 8/23/2024
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- **Version:** 1.0
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- **License(s)**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
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- **Model Developers:** Neural Magic
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Quantized version of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M).
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It achieves an average score of 31.91 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 31.55.
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### Model Optimizations
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This model was obtained by quantizing the weights of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M) to INT4 data type.
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This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
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Only the weights of the linear operators within transformers blocks are quantized. Symmetric group-wise quantization is applied, in which a linear scaling per group maps the INT4 and floating point representations of the quantized weights.
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The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. Quantization is performed with 10% damping factor, group-size as 64 and 512 sequences sampled from [LLM Compression Calibration](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
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## Creation
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This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
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```python
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from transformers import AutoTokenizer
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy
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from datasets import load_dataset
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import random
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model_id = "HuggingFaceTB/SmolLM-135M-Instruct"
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num_samples = 512
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max_seq_len = 4096
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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preprocess_fn = lambda example: {"text": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{text}".format_map(example)}
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dataset_name = "neuralmagic/LLM_compression_calibration"
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dataset = load_dataset(dataset_name, split="train")
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ds = dataset.shuffle().select(range(num_samples))
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ds = ds.map(preprocess_fn)
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examples = [
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tokenizer(
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example["text"], padding=False, max_length=max_seq_len, truncation=True,
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) for example in ds
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]
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# recipe = "w4a16_nohead_recipe.yaml"
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recipe = GPTQModifier(
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targets="Linear",
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scheme="W4A16",
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ignore=["lm_head"],
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dampening_frac=0.1,
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)
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model = SparseAutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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trust_remote_code=True
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)
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print(model)
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oneshot(
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model=model,
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dataset=ds,
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recipe=recipe,
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max_seq_length=max_seq_len,
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num_calibration_samples=num_samples,
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oneshot_device="cuda:1,2,3",
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)
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model_name = model_id.split("/")[-1]
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model.save_pretrained(f"{model_name}-quantized.w4a16")
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tokenizer.save_pretrained(f"{model_name}-quantized.w4a16")
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```
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## Evaluation
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The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [sparseML](https://github.com/neuralmagic/sparseml) engine, using the following command:
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```
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lm_eval \
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--model sparseml \
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--model_args pretrained=nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True \
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--tasks openllm \
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--batch_size auto
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```
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### Accuracy
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#### Open LLM Leaderboard evaluation scores
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<table>
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<tr>
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<td><strong>Benchmark</strong>
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</td>
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<td><strong>SmolLM-135M-Instruct </strong>
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</td>
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<td><strong>SmolLM-135M-Instruct-quantized.w4a16(this model)</strong>
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</td>
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<td><strong>Recovery</strong>
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</td>
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</tr>
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<tr>
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<td>MMLU (5-shot)
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</td>
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<td>26.220
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</td>
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<td>25.202
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</td>
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<td>96.12%
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</td>
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</tr>
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<tr>
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<td>ARC Challenge (25-shot)
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</td>
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<td>29.948
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</td>
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<td>30.034
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</td>
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<td>100.29%
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</td>
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</tr>
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<tr>
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<td>GSM-8K (5-shot, strict-match)
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</td>
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<td>1.289
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</td>
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<td>1.971
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</td>
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<td>152.91%
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</td>
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</tr>
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<tr>
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<td>Hellaswag (10-shot)
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</td>
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<td>41.41
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</td>
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<td>40.81
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</td>
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<td>98.55%
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</td>
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</tr>
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<tr>
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<td>Winogrande (5-shot)
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</td>
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<td>50.039
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</td>
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<td>53.591
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</td>
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<td>107.10%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot)
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</td>
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<td>40.38
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</td>
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<td>39.87
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</td>
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<td>98.74%
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</td>
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</tr>
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<tr>
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<td><strong>Average</strong>
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</td>
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<td><strong>31.55</strong>
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</td>
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<td><strong>31.91</strong>
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</td>
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<td><strong>101.16%</strong>
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</td>
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</tr>
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</table>
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