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Model: RedHatAI/Llama-3.2-1B-Instruct-quantized.w8a8 Source: Original Platform
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---
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license: llama3.2
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language:
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- en
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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pipeline_tag: text-generation
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tags:
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- llama
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- llama-3
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- neuralmagic
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- llmcompressor
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base_model: meta-llama/Llama-3.2-1B-Instruct
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---
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# Llama-3.2-1B-Instruct-quantized.w8a8
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## Model Overview
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- **Model Architecture:** Llama-3
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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 multiple languages. Similarly to [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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).
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- **Release Date:** 9/25/2024
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- **Version:** 1.0
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- **License(s):** Llama3.2
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- **Model Developers:** Neural Magic
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Quantized version of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
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It achieves scores within 5% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA.
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### Model Optimizations
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This model was obtained by quantizing the weights of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) to INT8 data type.
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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).
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Weight quantization also reduces disk size 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 [SmoothQuant](https://arxiv.org/abs/2211.10438) algorithm is used to alleviate outliers in the activations, whereas rhe [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization.
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Both algorithms are implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
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GPTQ used a 1% damping factor and 512 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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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/Llama-3.2-1B-Instruct-quantized.w8a8"
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number_gpus = 1
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max_model_len = 8192
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sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
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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 aslo 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 load_dataset
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier, SmoothQuantModifier
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model_id = "meta-llama/Llama-3.2-1B-Instruct"
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num_samples = 512
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max_seq_len = 8192
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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 = [
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SmoothQuantModifier(
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smoothing_strength=0.7,
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mappings=[
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[["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
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[["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"],
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[["re:.*down_proj"], "re:.*up_proj"],
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],
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),
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GPTQModifier(
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sequential=True,
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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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]
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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("Llama-3.2-1B-Instruct-quantized.w8a8")
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```
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## Evaluation
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The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA.
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Evaluation was conducted using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
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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](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals).
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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>Llama-3.2-1B-Instruct </strong>
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</td>
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<td><strong>Llama-3.2-1B-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>47.66
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</td>
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<td>47.95
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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>MMLU (CoT, 0-shot)
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</td>
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<td>47.10
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</td>
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<td>44.63
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</td>
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<td>94.8%
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</td>
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</tr>
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<tr>
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<td>ARC Challenge (0-shot)
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</td>
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<td>58.36
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</td>
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<td>56.14
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</td>
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<td>96.2%
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</td>
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</tr>
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<tr>
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<td>GSM-8K (CoT, 8-shot, strict-match)
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</td>
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<td>45.72
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</td>
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<td>46.70
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</td>
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<td>102.2%
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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>61.01
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</td>
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<td>60.95
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</td>
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<td>99.9%
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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>62.27
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</td>
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<td>61.33
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</td>
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<td>98.5%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot, mc2)
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</td>
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<td>43.52
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</td>
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<td>42.84
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</td>
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<td>98.4%
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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>52.24</strong>
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</td>
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<td><strong>51.51</strong>
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</td>
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<td><strong>98.7%</strong>
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</td>
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</tr>
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</table>
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### Reproduction
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The results were obtained using the following commands:
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#### MMLU
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU-CoT
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \
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--tasks mmlu_cot_0shot_llama_3.1_instruct \
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--apply_chat_template \
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--num_fewshot 0 \
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--batch_size auto
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```
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#### ARC-Challenge
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \
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--tasks arc_challenge_llama_3.1_instruct \
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--apply_chat_template \
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--num_fewshot 0 \
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--batch_size auto
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```
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#### GSM-8K
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \
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--tasks gsm8k_cot_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 8 \
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--batch_size auto
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```
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#### Hellaswag
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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--tasks hellaswag \
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--num_fewshot 10 \
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--batch_size auto
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```
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#### Winogrande
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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--tasks winogrande \
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--num_fewshot 5 \
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--batch_size auto
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|
```
|
||||||
|
|
||||||
|
#### TruthfulQA
|
||||||
|
```
|
||||||
|
lm_eval \
|
||||||
|
--model vllm \
|
||||||
|
--model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
|
||||||
|
--tasks truthfulqa \
|
||||||
|
--num_fewshot 0 \
|
||||||
|
--batch_size auto
|
||||||
|
```
|
||||||
3
config.json
Normal file
3
config.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3036fad522e142db0a0484a8b5258ac0fdcf676985f77f90701a31add23427af
|
||||||
|
size 2032
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 128000,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
128001,
|
||||||
|
128008,
|
||||||
|
128009
|
||||||
|
],
|
||||||
|
"temperature": 0.6,
|
||||||
|
"top_p": 0.9,
|
||||||
|
"transformers_version": "4.44.1"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:cb63ec84f5b37b22601df876df8a0871c85214189988e9f568ffa4e4cd57c147
|
||||||
|
size 2024670536
|
||||||
18
recipe.yaml
Normal file
18
recipe.yaml
Normal file
@@ -0,0 +1,18 @@
|
|||||||
|
quant_stage:
|
||||||
|
quant_modifiers:
|
||||||
|
SmoothQuantModifier:
|
||||||
|
smoothing_strength: 0.7
|
||||||
|
mappings:
|
||||||
|
- - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
|
||||||
|
- re:.*input_layernorm
|
||||||
|
- - ['re:.*gate_proj', 're:.*up_proj']
|
||||||
|
- re:.*post_attention_layernorm
|
||||||
|
- - ['re:.*down_proj']
|
||||||
|
- re:.*up_proj
|
||||||
|
GPTQModifier:
|
||||||
|
sequential_update: true
|
||||||
|
dampening_frac: 0.01
|
||||||
|
ignore: [lm_head]
|
||||||
|
scheme: W8A8
|
||||||
|
targets: [Linear]
|
||||||
|
observer: mse
|
||||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|begin_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|eot_id|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": "<|eot_id|>"
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:4745787bf5429f4558dbadb95086d68ccc290ca1fac62bdb3d05c233fab5bc40
|
||||||
|
size 9085756
|
||||||
3
tokenizer_config.json
Normal file
3
tokenizer_config.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:df757249013ee916c1318f170d2763cc2272d10d66c5d73a792ce34c2bd8cbb6
|
||||||
|
size 54557
|
||||||
Reference in New Issue
Block a user