on_quantization.md

A concise summary with specific recommendations for quantizing your large language models (LLMs):

When working with multilingual quantization for large language models (LLMs), the number of samples needed for effective quantization increases with the number of target languages. With more linguistic features, the model must learn and adapt across a broader spectrum during the quantization process.

Recent work, such as the Lens framework and studies on quantized multilingual LLMs, emphasizes that larger datasets are critical for multilingual models to ensure performance remains consistent across all languages. These models typically perform best when they have sufficient samples for each language. This allows them to maintain their accuracy in quantization. In the case of multilingual evaluation tasks, several sources highlight that adding more languages requires proportional increases in calibration samples to smooth activations and avoid performance drops. These studies often mention the use of thousands of samples per language to preserve accuracy during multilingual post-training quantization (Lens, 2024, Quantization for Multilingual LLMs, 2024).

Instruction Fine-tuning and Evaluation

Instruction fine-tuning has become crucial to enhance language models' ability to follow specific instructions and perform diverse tasks (Chung et al., 2022) and especially chat interations. It typically involves training on datasets consisting of instruction-output pairs, which can be manually curated, transformed from existing datasets, or generated using other models.

The evaluation of instruction-tuned models often requires specialized methods (Honovich et al., 2023). These methods focus on assessing the model's ability to follow instructions and generate appropriate responses, rather than relying solely on general metrics like perplexity.

Contrary to some assumptions, there is no established requirement or practice of including instruction data in the input matrix (imatrix) used for perplexity testing or other general evaluations (Wei et al., 2022). The evaluation of instruction-tuned models typically involves task-specific metrics and methods that directly measure instruction-following capabilities.

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With Salamandra models, the following recommendations can be made:

1. Use Calibration Data

For post-training quantization (PTQ), gather several thousand calibration samples per task. This helps smooth activations and adjust weights to avoid performance loss (SmoothQuant, Comprehensive Evaluation).

2. Dataset Size Recommendations

• For 2B models (Base or Instruct): Start with 1,000 to 5,000 samples per language for quantization.
• For 7B models (Base or Instruct): Start with 5,000 to 20,000 samples per language.
• For 40B models (Base or Instruct): Start with 20,000 to 100,000 samples per language

(SmoothQuant, QLLM).

3. Balance Languages

• For multilingual models, ensure you gather balanced datasets across languages. If resources are limited, start with a minimum of 1,000 samples per language and adjust based on performance (QLLM).

4. Outlier Handling in Large Models

For models over 7B parameters, address outliers in activations using methods like channel-wise quantization. Larger models require more robust outlier handling, which can be mitigated by using enough calibration data (QLLM, SmoothQuant).

note: llama.cpp supports several quantization methods, including row-wise and block-wise quantization schemes but there is no ready support for channel-wise quantization.

5. Start Small and Scale

Begin with smaller datasets, evaluate the quantized model’s performance, and scale up as needed. Add more samples if you see significant drops in accuracy or performance after quantization (Comprehensive Evaluation, DataCamp, 2023).

note: This is beyond the scope of the work in this repo.

This work

We have many languages. We could measure the rate of change in PPL for one model each of Q8_0, q4_K_M, and iq3_K starting at, say 10 samples/language to some intermediate (say 200, assuming we sample enough intermediate steps to feel we have the rate of change nailed down), then predict PPL at 1k samples. If the PPL is small than expected, we are reaching diminishing returns and can stop increasing. However, as a first attempt we will only quantize to the minimums in the range.

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References

1. SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

   • Authors: Xiao et al.
   • Year: 2023
   • Source: arXiv
   • Summary: This paper addresses activation outliers in large models and recommends using calibration samples for effective quantization.

2. QLLM: Accurate and Efficient Low-bitwidth Quantization for LLMs

   • Authors: Liu et al.
   • Year: 2024
   • Source: ICLR
   • Summary: QLLM focuses on outlier handling and low-bitwidth quantization for models like LLaMA, recommending balanced datasets and channel-wise techniques.

3. A Comprehensive Evaluation of Quantization Strategies for Large Language Models

   • Authors: Jin et al.
   • Year: 2024
   • Source: ACL Anthology
   • Summary: Provides a thorough evaluation of quantization strategies on various LLMs, noting that several thousand samples per task are often needed.

4. Quantization for Large Language Models (LLMs): Reduce AI Model Sizes Efficiently

   • Year: 2023
   • Source: DataCamp
   • Summary: Introduces practical methods for quantizing models and discusses dataset requirements for ensuring performance.

5. Lens: Rethinking Multilingual Enhancement for Large Language Models

• Authors: Zhao, Weixiang, et al.
• Year: 2024
• Source: arXiv
• Summary: This study emphasizes that as the number of languages increases, the number of samples required for quantization grows. Multilingual models need larger datasets to maintain performance across all languages. The authors recommend scaling the number of samples per language as the model size and the number of target languages increase.

6 How Does Quantization Affect Multilingual LLMs?

• Authors: Ahmadian et al.
• Year: 2024
• Source: arXiv
• Summary: This paper explores the impact of quantization on multilingual LLMs. It highlights the need for larger datasets as the number of target languages increases and suggests using several thousand calibration samples per language to mitigate performance degradation.

7 Emergent Abilities of Large Language Models

• Authors: Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., ... & Fedus, W.
• Year: 2022
• Source: arXiv
• Summary: This paper investigates emergent abilities in large language models as they scale in size. The authors demonstrate how model capabilities appear unexpectedly at certain scale thresholds.

8 Scaling Instruction-Finetuned Language Models

• Authors: Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., ... & Le, Q. V.
• Year: 2022
• Source: arXiv
• Summary: The authors explore the scaling of instruction-finetuned language models and their impact on downstream task performance, showing how larger models benefit from instruction tuning.

9 Instruction Tuning for Large Language Models: A Survey

• Authors: Honovich, O., Shaham, U., Bowman, S. R., & Levy, O.
• Year: 2023
• Source: arXiv
• Summary: This survey paper provides a comprehensive overview of instruction tuning for large language models, summarizing recent advances and challenges in optimizing models for specific instructions.