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Model: neuralmagic/SmolLM-360M-Instruct-quantized.w8a8 Source: Original Platform
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README.md
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- int8
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- vllm
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base_model: HuggingFaceTB/SmolLM-360M-Instruct
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---
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# SmolLM-360M-Instruct-quantized.w8a8
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## Model Overview
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- **Model Architecture:** Llama
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** INT8
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- **Weight quantization:** INT8
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- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [SmolLM-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct), 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:** 8/22/2024
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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-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct).
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It achieves an average score of 35.49 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 35.15.
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### Model Optimizations
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This model was obtained by quantizing the weights of [SmolLM-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct) to INT8 data type.
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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Only weights and activations of the linear operators within transformers blocks are quantized.
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Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
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Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
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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.
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GPTQ used a 1% damping factor and 1,024 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
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## Deployment
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### Use with vLLM
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "neuralmagic/SmolLM-360M-Instruct-quantized.w8a8"
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sampling_params = SamplingParams(temperature=0.6, top_p=0.92, max_tokens=100)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": "List the steps to bake a chocolate cake from scratch."},
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]
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prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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llm = LLM(model=model_id)
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outputs = llm.generate(prompts, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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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 datasets import Dataset
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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import random
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model_id = "HuggingFaceTB/SmolLM-360M-Instruct"
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num_samples = 1024
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max_seq_len = 2048
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def preprocess_fn(example):
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return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
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ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
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ds = ds.shuffle().select(range(num_samples))
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ds = ds.map(preprocess_fn)
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recipe = GPTQModifier(
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targets="Linear",
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scheme="W8A8",
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ignore=["lm_head"],
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dampening_frac=0.01,
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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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)
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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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)
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model.save_pretrained("SmolLM-360M-Instruct-quantized.w8a8")
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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 [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/SmolLM-360M-Instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \
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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-360M-Instruct-quantized</strong>
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</td>
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<td><strong>SmolLM-360M-Instruct-quantized.w8a8 (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>25.69
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</td>
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<td>25.77
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</td>
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<td>100.3%
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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>37.46
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</td>
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<td>38.05
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</td>
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<td>101.6%
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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>2.05
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</td>
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<td>1.44
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</td>
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<td>70.4%
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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>51.72
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</td>
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<td>52.02
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</td>
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<td>100.6%
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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>55.25
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</td>
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<td>55.41
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</td>
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<td>100.3%
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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>38.76
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</td>
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<td>40.22
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</td>
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<td>103.8%
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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>35.15</strong>
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</td>
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<td><strong>35.49</strong>
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</td>
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<td><strong>101.6%</strong>
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</td>
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</tr>
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</table>
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config.json
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{
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"_name_or_path": "/root/.cache/huggingface/hub/models--HuggingFaceTB--SmolLM-360M-Instruct/snapshots/73b7144f76331266f5f45d5642fd8da653583b13",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 960,
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"initializer_range": 0.02,
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"intermediate_size": 2560,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 15,
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"num_hidden_layers": 32,
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"num_key_value_heads": 5,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.1",
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"use_cache": true,
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"vocab_size": 49152,
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"quantization_config": {
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"config_groups": {
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"group_0": {
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"input_activations": {
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"block_structure": null,
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"dynamic": true,
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"group_size": null,
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"num_bits": 8,
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"observer": "memoryless",
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"observer_kwargs": {},
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"strategy": "token",
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"symmetric": true,
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"type": "int"
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},
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"output_activations": null,
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"targets": [
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"Linear"
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],
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"weights": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "channel",
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"symmetric": true,
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"type": "int"
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}
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}
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},
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"format": "int-quantized",
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"global_compression_ratio": 1.2392030626348693,
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"ignore": [
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"lm_head"
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],
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"kv_cache_scheme": null,
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"quant_method": "compressed-tensors",
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"quantization_status": "compressed"
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}
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_new_tokens": 40,
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"pad_token_id": 2,
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"transformers_version": "4.44.1"
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}
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merges.txt
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9e10091b239ca3f8324d124299f98ae58d81ea7fc9ea40cf1eea4f445c9de75
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size 504052632
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recipe.yaml
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quant_stage:
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quant_modifiers:
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SmoothQuantModifier:
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smoothing_strength: 0.8
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mappings:
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- - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
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- re:.*input_layernorm
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- - ['re:.*gate_proj', 're:.*up_proj']
|
||||||
|
- re:.*post_attention_layernorm
|
||||||
|
- - ['re:.*down_proj']
|
||||||
|
- re:.*up_proj
|
||||||
|
GPTQModifier:
|
||||||
|
sequential_update: false
|
||||||
|
dampening_frac: 0.01
|
||||||
|
ignore: [lm_head]
|
||||||
|
scheme: W8A8
|
||||||
|
targets: Linear
|
||||||
|
observer: mse
|
||||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
106
test_prompts.py
Normal file
106
test_prompts.py
Normal file
@@ -0,0 +1,106 @@
|
|||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
|
||||||
|
BASE_PATH = "/fsx/loubna/projects/alignment-handbook/recipes/cosmo2/sft/data"
|
||||||
|
TEMPERATURE = 0.2
|
||||||
|
TOP_P = 0.9
|
||||||
|
|
||||||
|
CHECKPOINT = "loubnabnl/smollm-350M-instruct-add-basics"
|
||||||
|
|
||||||
|
print(f"💾 Loading the model and tokenizer: {CHECKPOINT}...")
|
||||||
|
device = "cuda"
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
|
||||||
|
model_s = AutoModelForCausalLM.from_pretrained(CHECKPOINT).to(device)
|
||||||
|
|
||||||
|
print("🧪 Testing single-turn conversations...")
|
||||||
|
L = [
|
||||||
|
"Hi",
|
||||||
|
"Hello",
|
||||||
|
"Tell me a joke",
|
||||||
|
"Who are you?",
|
||||||
|
"What's your name?",
|
||||||
|
"How do I make pancakes?",
|
||||||
|
"Can you tell me what is gravity?",
|
||||||
|
"What is the capital of Morocco?",
|
||||||
|
"What's 2+2?",
|
||||||
|
"Hi, what is 2+1?",
|
||||||
|
"What's 3+5?",
|
||||||
|
"Write a poem about Helium",
|
||||||
|
"Hi, what are some popular dishes from Japan?",
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
for i in range(len(L)):
|
||||||
|
print(f"🔮 {L[i]}")
|
||||||
|
messages = [{"role": "user", "content": L[i]}]
|
||||||
|
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
|
||||||
|
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
|
||||||
|
outputs = model_s.generate(
|
||||||
|
inputs, max_new_tokens=200, top_p=TOP_P, do_sample=True, temperature=TEMPERATURE
|
||||||
|
)
|
||||||
|
with open(
|
||||||
|
f"{BASE_PATH}/{CHECKPOINT.split('/')[-1]}_temp_{TEMPERATURE}_topp{TOP_P}.txt",
|
||||||
|
"a",
|
||||||
|
) as f:
|
||||||
|
f.write("=" * 50 + "\n")
|
||||||
|
f.write(tokenizer.decode(outputs[0]))
|
||||||
|
f.write("\n")
|
||||||
|
|
||||||
|
|
||||||
|
print("🧪 Now testing multi-turn conversations...")
|
||||||
|
# Multi-turn conversations
|
||||||
|
messages_1 = [
|
||||||
|
{"role": "user", "content": "Hi"},
|
||||||
|
{"role": "assistant", "content": "Hello! How can I help you today?"},
|
||||||
|
{"role": "user", "content": "What's 2+2?"},
|
||||||
|
]
|
||||||
|
messages_2 = [
|
||||||
|
{"role": "user", "content": "Hi"},
|
||||||
|
{"role": "assistant", "content": "Hello! How can I help you today?"},
|
||||||
|
{"role": "user", "content": "What's 2+2?"},
|
||||||
|
{"role": "assistant", "content": "4"},
|
||||||
|
{"role": "user", "content": "Why?"},
|
||||||
|
]
|
||||||
|
messages_3 = [
|
||||||
|
{"role": "user", "content": "Who are you?"},
|
||||||
|
{"role": "assistant", "content": "I am an AI assistant. How can I help you today?"},
|
||||||
|
{"role": "user", "content": "What's your name?"},
|
||||||
|
]
|
||||||
|
messages_4 = [
|
||||||
|
{"role": "user", "content": "Tell me a joke"},
|
||||||
|
{"role": "assistant", "content": "Sure! Why did the tomato turn red?"},
|
||||||
|
{"role": "user", "content": "Why?"},
|
||||||
|
]
|
||||||
|
messages_5 = [
|
||||||
|
{"role": "user", "content": "Can you tell me what is gravity?"},
|
||||||
|
{
|
||||||
|
"role": "assistant",
|
||||||
|
"content": "Sure! Gravity is a force that attracts objects toward each other. It is what keeps us on the ground and what makes things fall.",
|
||||||
|
},
|
||||||
|
{"role": "user", "content": "Who discovered it?"},
|
||||||
|
]
|
||||||
|
messages_6 = [
|
||||||
|
{"role": "user", "content": "How do I make pancakes?"},
|
||||||
|
{
|
||||||
|
"role": "assistant",
|
||||||
|
"content": "Sure! Here is a simple recipe for pancakes: Ingredients: 1 cup flour, 1 cup milk, 1 egg, 1 tbsp sugar, 1 tsp baking powder, 1/2 tsp salt. Instructions: 1. Mix all the dry ingredients together in a bowl. 2. Add the milk and egg and mix until smooth. 3. Heat a non-stick pan over medium heat. 4. Pour 1/4 cup of batter onto the pan. 5. Cook until bubbles form on the surface, then flip and cook for another minute. 6. Serve with your favorite toppings.",
|
||||||
|
},
|
||||||
|
{"role": "user", "content": "What are some popular toppings?"},
|
||||||
|
]
|
||||||
|
|
||||||
|
L = [messages_1, messages_2, messages_3, messages_4, messages_5, messages_6]
|
||||||
|
|
||||||
|
for i in range(len(L)):
|
||||||
|
input_text = tokenizer.apply_chat_template(L[i], tokenize=False)
|
||||||
|
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
|
||||||
|
outputs = model_s.generate(
|
||||||
|
inputs, max_new_tokens=200, top_p=TOP_P, do_sample=True, temperature=TEMPERATURE
|
||||||
|
)
|
||||||
|
with open(
|
||||||
|
f"{BASE_PATH}/{CHECKPOINT.split('/')[-1]}_temp_{TEMPERATURE}_topp{TOP_P}_MT.txt",
|
||||||
|
"a",
|
||||||
|
) as f:
|
||||||
|
f.write("=" * 50 + "\n")
|
||||||
|
f.write(tokenizer.decode(outputs[0]))
|
||||||
|
f.write("\n")
|
||||||
|
|
||||||
|
print("🔥 Done!")
|
||||||
98254
tokenizer.json
Normal file
98254
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
154
tokenizer_config.json
Normal file
154
tokenizer_config.json
Normal file
@@ -0,0 +1,154 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"3": {
|
||||||
|
"content": "<repo_name>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"4": {
|
||||||
|
"content": "<reponame>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"5": {
|
||||||
|
"content": "<file_sep>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"6": {
|
||||||
|
"content": "<filename>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"7": {
|
||||||
|
"content": "<gh_stars>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"8": {
|
||||||
|
"content": "<issue_start>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"9": {
|
||||||
|
"content": "<issue_comment>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"10": {
|
||||||
|
"content": "<issue_closed>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"11": {
|
||||||
|
"content": "<jupyter_start>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"12": {
|
||||||
|
"content": "<jupyter_text>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"13": {
|
||||||
|
"content": "<jupyter_code>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"14": {
|
||||||
|
"content": "<jupyter_output>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"15": {
|
||||||
|
"content": "<jupyter_script>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"16": {
|
||||||
|
"content": "<empty_output>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": "<|im_start|>",
|
||||||
|
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"model_max_length": 2048,
|
||||||
|
"pad_token": "<|im_end|>",
|
||||||
|
"tokenizer_class": "GPT2Tokenizer",
|
||||||
|
"unk_token": "<|endoftext|>",
|
||||||
|
"vocab_size": 49152
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user