Live Weight Adjustment on LLM Llama AI

AI Generated image representing LLM weights.

AI - LLM's have always fascinated me, and when we learned about the actual functionality and indeed the files and programs that are used to make a LLM work on computer hardware, some questions came to me.

Today I tried to put together a real-world example of a open-source, open-weight LLM adjustment that we have access to: Meta's Llama AI. Specifically the 2 series.

Several methods and considerations emerge for adjusting or modifying the weights (or settings) of an AI language model like an LLM during operation, particularly for fine-tuning.

Below is a list of known different ways an LLM's weights can be adjusted.

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1. Direct Weight Scaling Between Activated Neurons

• Description: Downscale or upscale weights between neurons that were activated during the generation of an answer, based on user feedback (e.g., "answer is bad" or "answer is good").
• Process:
  • Identify neurons activated during the generation of a "bad" answer.
  • Reduce (downscale) the weights connecting these neurons to discourage the same output.
  • Optionally, upscale weights for a "good" answer to reinforce it.
• Challenges:
  • Requires real-time tracking of which neurons are activated.
  • May damage unrelated knowledge if too many weights are modified indiscriminately.
• Suggested Tools:
• Modify the model's activation function (e.g., SiLU) to capture and adjust weights on the fly.
• Use frameworks like llama.cpp to access and manipulate tensors between layers.

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2. Layer-wise Tensor Manipulation

• Description: Inspect and modify the tensors (weight matrices) between layers during model execution using an API like llama.cpp.
• Process:
  • Pause execution between layers to analyze intermediate tensors.
  • Adjust specific values in the tensors based on the desired outcome.
• Challenges:
  • Complexity increases in GPU mode due to data transfer between CPU and GPU.
  • In CPU mode (e.g., with GGML), modifying tensors might lead to undefined behavior if the framework assumes they are constant.
• Considerations:
• Requires deep knowledge of the model's architecture.
• May be more feasible for CPU-based implementations.

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3. Selective Weight Adjustment Based on Intermediate Activations

• Description: Use intermediate activations (e.g., from articles like the one on LLaMA2 decoding) to pinpoint specific weights responsible for an output and adjust them selectively.
• Process:
  • Monitor activations between layers to identify which "neurons" (or tensor elements) contribute to a bad answer.
  • Modify only the weights connecting these activations to minimize collateral damage.
• Advantages:
  • More precise than blanket scaling of all activated weights.
  • Could preserve unrelated knowledge by targeting only relevant weights.
• Suggested Tools:
• Articles or tools analyzing intermediate activations (e.g., LLaMA2 decoding article referenced in this article: https://www.lesswrong.com/posts/fJE6tscjGRPnK8C2C/decoding-intermediate-activations-in-llama-2-7b ).
• Custom activation functions to track and modify weights dynamically.

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4. Statistical Weight Identification and Tuning

• Description: Identify weights responsible for specific outputs (e.g., digits like "1" vs. "7") by feeding the model multiple examples and analyzing activation patterns, then adjust those weights.
• Process:
  • Feed the model examples (e.g., sentences with "1" as the answer) to find consistently activated weights.
  • Downscale weights associated with incorrect outputs (e.g., "1") and upscale those for correct outputs (e.g., "7").
• Advantages:
  • Could fine-tune the model for specific tasks without broad disruption.
• Challenges:
• Requires extensive experimentation and statistical analysis to isolate relevant weights.
• May not scale easily to complex outputs beyond simple examples like digits.

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5. Steering Vector Approach

• Description: Modify embedding vectors at specific layers to influence the model's output, as demonstrated in SlyEcho’s steering vector experiment ( https://github.com/ggml-org/llama.cpp/pull/1472 ).
• Process:
  • Read the embedding vectors at a chosen layer.
  • Write modified values to "steer" the model's behavior toward a desired output.
• Advantages:
  • Targets higher-level representations rather than low-level weights, potentially simplifying the process.
• Challenges:
• The referenced implementation is outdated and requires adaptation to modern frameworks like llama.cpp.
• May not directly adjust weights but rather influences the model indirectly.

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6. Dynamic Activation Function Modification

• Description: Alter the model's activation function (e.g., SiLU) to detect activations, store them, and adjust corresponding weights during runtime.
• Process:
  • Modify the activation function to log which neurons activate for a given output.
  • On user feedback (e.g., "bad answer"), re-run the prompt with adjusted weights via the modified activation function.
• Advantages:
  • Allows for minimal, targeted changes to weights, potentially preserving overall model integrity.
• Challenges:
• Requires custom implementation of the activation function.
• Feasibility depends on the model’s framework and runtime environment.

What is SiLU?

SiLU, or Sigmoid Linear Unit, is an activation function used in neural networks, including large language models (LLMs). It combines the properties of the sigmoid function and a linear transformation to introduce non-linearity while maintaining smooth gradients. SiLU was introduced as an improvement over earlier activation functions like ReLU (Rectified Linear Unit) due to its differentiable nature and ability to handle both positive and negative inputs effectively.

Definition

The SiLU function is mathematically defined as:\text{SiLU}(x) = x \cdot \sigma(x)

Where:

• ( x ) is the input value.
• \sigma(x) = \frac{1}{1 + e^{-x}}

  is the sigmoid function, which maps any real value into the range ((0, 1)).

In simpler terms, SiLU multiplies the input ( x ) by its sigmoid-transformed value, producing an output that is zero or negative for negative inputs and grows smoothly for positive inputs.

Properties

1. Non-Linearity: Like other activation functions, SiLU introduces non-linearity into the network, allowing it to learn complex patterns.
2. Smoothness: Unlike ReLU, which has a sharp cutoff at zero, SiLU is continuously differentiable, leading to more stable gradients during backpropagation.
3. Output Range:
   • For negative ( x ), the output approaches zero but remains slightly negative.
   • For positive ( x ), the output grows roughly linearly but is tempered by the sigmoid factor.
4. Self-Gating: The multiplication by

   \sigma(x)

   acts as a "gate" that scales the input based on its magnitude, giving the function adaptive behavior.

Example Values

• \text{SiLU}(-2) = -2 \cdot \frac{1}{1 + e^2} \approx -0.27
• \text{SiLU}(0) = 0 \cdot \frac{1}{1 + e^0} = 0
• \text{SiLU}(2) = 2 \cdot \frac{1}{1 + e^{-2}} \approx 1.76

Comparison to Other Activation Functions

• ReLU (

  \max(0, x)

  ): SiLU avoids the "dying ReLU" problem (where neurons output zero for all inputs) by allowing small negative outputs.
• Sigmoid: Unlike the standalone sigmoid, SiLU preserves the magnitude of ( x ) rather than squashing it into ((0, 1)).
• Swish: SiLU is actually identical to the Swish activation function (proposed by Google researchers in 2017), though Swish sometimes includes a trainable parameter

  \beta

  as

  x \cdot \sigma(\beta x)

  . When

  \beta = 1

  , Swish reduces to SiLU.

Use in LLMs

SiLU is commonly used in modern transformer-based models (e.g., some variants of LLaMA or BERT derivatives) because it provides better performance than ReLU in deep networks. Its smoothness and ability to retain gradient information make it particularly suitable for training large, complex models where vanishing gradients can be an issue.

Relevance to Weight Adjustment

In the context of the discussion about modifying weights (e.g., in issue #2654), unwnstr suggested modifying the SiLU activation function to track which neurons activate during a response and adjust their weights dynamically. By embedding logic into SiLU, one could:

• Log activations to identify "active" neurons.
• Scale weights connecting these neurons based on feedback (e.g., reducing weights for a "bad answer"). This approach leverages SiLU’s role as a gatekeeper in the network to enable live fine-tuning.

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7. CPU vs. GPU-Specific Adjustments

• Description: Tailor weight modification strategies based on whether the model runs on CPU or GPU.
• Process:
  • CPU Mode: Easier to modify tensors directly (e.g., using GGML in llama.cpp), though assumptions about tensor constancy must be checked.
  • GPU Mode: Requires managing data transfers between CPU and GPU, complicating real-time adjustments.
• Considerations:
• CPU-based adjustments may be more practical for experimental fine-tuning.

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Key Considerations for All Methods:

• Precision: Modifying too many weights (e.g., all activated neurons) risks damaging unrelated knowledge, while too few changes may not affect the output.
• Architecture Knowledge: Effective weight adjustment requires understanding the specific LLM’s structure (e.g., layers, tensors, activation functions).
• Feedback Loop: Most methods rely on user feedback ("bad answer" or "good answer") to guide adjustments.
• Scalability: These approaches may work for fine-tuning but are impractical for training from scratch.

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Tools and Resources Mentioned:

• llama.cpp API: For accessing and manipulating tensors or layers.
• GGML: A framework for CPU-based model execution, potentially easier for weight modification.
• Intermediate Activation Analysis: Articles like the LLaMA2 decoding post on LessWrong for understanding activations.
• Steering Vector Experiment (PR #1472): An example of embedding-based adjustments.

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These methods represent experimental and creative ways to adjust an LLM’s weights, focusing on live fine-tuning rather than traditional training. Each approach has trade-offs in complexity, precision, and feasibility, and most require significant technical expertise to implement effectively.

Source Discussion: https://github.com/ggml-org/llama.cpp/discussions/2654

Hi. I'm alby13. AI Scientist fox. And yes, now that I have gotten older, I do wear glasses for reading. Thanks for stopping by, and reading this, even! I work with AI every day. Make sure you follow me on X at alby13, and if you can support me in any way it would mean the world to me. #Accelerate