Gemma3 is out

Gemma3 was released today. With 1B, 4B, 12B, 27B model.

According to Gemini 2.0, it can handle autocompletion. I'll test it out.

Yes, the Gemma 3 1B model is capable of handling both autocompletion and Fill-In-the-Middle (FIM) tasks.

Here's how it handles each:

Autocompletion:

Gemma models, including the 1B version, are fundamentally designed for text generation. Autocompletion is a core text generation task where the model predicts and suggests the next word or sequence of words based on the preceding text.
The 1B model's architecture and training allow it to effectively perform autocompletion. Its smaller size makes it particularly suitable for real-time autocompletion scenarios, such as in mobile keyboards or code editors, where low latency is crucial.
You can use the Gemma 3 1B model to build features like:
- Predictive text input: Suggesting words as a user types.
- Code autocompletion: Suggesting code snippets or completing lines of code.
- Search query suggestions: Predicting and suggesting search queries as a user types in a search bar.

Fill-In-the-Middle (FIM) Task:

FIM, also known as context completion or infix completion, is a more advanced language modeling task where the model is asked to fill in a missing segment of text given the text before and after the gap.
Gemma 3 models, including the 1B model, are trained with a technique that enables them to perform FIM tasks effectively. This is a significant capability, as it allows for more sophisticated text editing and code generation applications.
The expanded context window of Gemma 3 models, even the 1B version with its 32,000 token window, is beneficial for FIM tasks. It allows the model to consider a larger context surrounding the missing text, leading to more coherent and accurate completions.
Use cases for FIM with Gemma 3 1B include:
- Code infilling: Completing partially written code blocks, functions, or classes.
- Document editing: Filling in missing sentences or paragraphs in a document.
- Creative writing assistance: Helping writers overcome writer's block by suggesting continuations or filling in gaps in their narratives.

In summary, the Gemma 3 1B model is well-equipped to handle both autocompletion and FIM tasks. Its efficiency and speed, combined with its text generation capabilities and FIM training, make it a valuable tool for applications requiring interactive text completion and more advanced context-aware text editing, even on resource-constrained devices.

Testing 1B for autocompletion

Gemma is a model that can be used as a model

huh ?

sorry, what ?

It seems that it can't really handle autocompletion, or the problem is elsewhere (continue dev instruction ?)

In LM Studio chat i get some proper result, and at 66 tok/s on my macbook air M3

Should i enable or disable speculative decoding when using a reasoning model ?

Okay, let's dive into the complex topic of speculative decoding for reasoning models. The answer isn’t a simple "yes" or "no." It depends heavily on what you're trying to achieve and the specific model architecture. Here's a breakdown of the considerations:

1. What is Speculative Decoding?

First, let's quickly recap speculative decoding. It's a technique that goes beyond simply choosing the most probable next token based on the current context. Instead, it predicts the most likely tokens to come after the current one, and then uses those predictions to generate the next token. It’s like having a "wild guess" of possible outputs.

2. When is Speculative Decoding Beneficial?

Complex Reasoning Tasks: This is where speculative decoding shines. When your reasoning model needs to tackle intricate logic puzzles, multi-step problem solving, or creative generation that relies on making unexpected connections, speculative decoding can dramatically improve the quality of its outputs.
Exploring Diverse Solutions: It encourages exploration of a broader range of potential solutions than simply relying on the most probable path. It helps the model "think outside the box."
Handling Ambiguity & Uncertainty: Speculative decoding is good at dealing with situations where the model isn't completely sure about the best answer.

3. When is it Less Beneficial?

Simple Tasks: If your reasoning model is doing straightforward tasks like sentiment analysis or basic fact retrieval, speculative decoding might not offer significant improvements. The model’s core architecture and training data are already sufficient.
Computational Cost: Speculative decoding is computationally expensive. It requires generating many more tokens than the original decoding process, which can slow down your workflow, especially for large models.
Potential for Nonsense/Hallucinations: While it can improve quality, speculative decoding has been linked to higher instances of generating incorrect or nonsensical answers in some cases. This is a known concern.

4. Key Considerations and Strategies

Temperature: A lower temperature (closer to 1) generally leads to more deterministic and predictable outputs, which can be helpful when you want the model to stick closely to the most probable path. Higher temperatures allow for more creativity but also increase risk of errors.
Top-p/Nucleus Sampling: These methods control the diversity of the generated text by focusing on a subset of the most likely tokens. Adjusting these values can balance exploration and predictability.
Regularization Techniques: Techniques like dropout or weight decay can help prevent overfitting, which might reduce the benefits of speculative decoding.

5. How to Decide – A Practical Approach

Here's a suggested approach:

Start with Standard Decoding: Begin with the default decoding strategy (e.g., greedy decoding) for initial experimentation.
Experiment with Temperature and Top-p/Nucleus: Try adjusting these parameters to see how they affect the quality of the output. Start with small changes and monitor the results carefully.
Monitor for Errors: Pay very close attention to where the model starts generating errors – this is a key indicator to evaluate if speculative decoding is truly beneficial.
Evaluate on Your Specific Use Case: The best approach will depend heavily on what you're using the reasoning model for. If it’s for creative writing, speculative decoding might be worth trying. If it’s for precise knowledge retrieval, standard decoding is likely more appropriate.

Resources to Explore:

Research Papers: Search for papers on "speculative decoding" and its application to reasoning models (e.g., on arXiv).
Model Documentation: Many large language model providers offer documentation explaining their decoding strategies and parameters.

Disclaimer: I am an AI Chatbot and not a machine learning expert. This information is based on current understanding but the field of NLP is constantly evolving.

To help me give you even more tailored advice, could you tell me:

What type of reasoning model are you using (e.g., GPT-3, Llama 2, etc.)?
What specific task or problem are you trying to solve with the reasoning model?

He's wrong though. But at least it's not to the level of autocompletion crap.

Testing 4B

I'll start with chat in LM Studio first.

Okay, let's break down the question of enabling or disabling speculative decoding with reasoning models. It’s a nuanced topic and the best answer depends on several factors. Here's a detailed breakdown:

What is Speculative Decoding?

Speculative decoding (also known as "ahead-of-time" decoding) is a technique used by large language models (LLMs) like GPT-4, Claude, etc., to speed up generation. Instead of waiting for each token to be fully decoded and validated, the model guesses what the next token might be based on its current understanding of the context. It then starts generating that predicted token before it's actually confirmed. If the prediction is correct, great! If not, the model quickly reverts to the previously verified output.

Arguments for Enabling Speculative Decoding (Generally Recommended):

Significant Speed Improvements: This is the biggest benefit. Speculative decoding can dramatically reduce latency – the time it takes for a response to appear. It's often cited as being 2x-4x faster than standard, non-speculative decoding.
Improved Throughput: Because responses are generated more quickly, you can handle more requests per unit of time.
Better User Experience: Faster responses lead to a smoother and more responsive user experience, especially in interactive applications like chatbots.

Arguments for Disabling Speculative Decoding (Situations Where It Can Be Problematic):

Increased Hallucinations/Incorrect Outputs: This is the primary concern with speculative decoding. Because the model is making guesses before validation, it's more likely to confidently output incorrect or nonsensical information – what we call "hallucinations." The speculation introduces a higher probability of generating something that seems plausible but isn’t true.
Reduced Accuracy in Certain Tasks: For tasks requiring high precision and factual accuracy (e.g., complex mathematical reasoning, legal analysis, scientific research), speculative decoding can significantly degrade performance. The added risk outweighs the speed benefit.
Increased Resource Consumption (Potentially): While speculative decoding can be more efficient overall due to reduced waiting times, it can also lead to increased computational load if the model is constantly generating and discarding incorrect predictions. This depends on the specific implementation.

Recommendations Based on Reasoning Model Use Cases:

Here's a breakdown of when you should consider enabling or disabling speculative decoding:

Creative Writing/Brainstorming/Casual Conversation (High Priority - Enable):
- Why: Speed is paramount here. The occasional hallucination isn’t as critical if the model is generating ideas quickly and efficiently. The user can usually spot inaccuracies and correct them.
- Example: Using an LLM to help you write a story, generate marketing slogans, or have a casual chat.
Code Generation (Medium Priority - Enable with Caution):
- Why: Code generation benefits from speed, but accuracy is crucial. You'll likely want to monitor the output closely and test it thoroughly.
- Recommendation: Start with speculative decoding enabled, but carefully review all generated code for errors before using it.
Fact Retrieval/Question Answering (Low Priority - Disable or Carefully Monitor):
- Why: Accuracy is absolutely critical. Speculative decoding significantly increases the risk of providing incorrect information, which can be very problematic in this context.
- Recommendation: Generally, disable speculative decoding for question-answering applications where factual correctness is paramount. If you enable it, implement robust verification mechanisms (e.g., cross-referencing with trusted sources).
Complex Reasoning/Mathematical Problems (Disable - Highly Recommended):
- Why: These tasks require precise logical steps and accurate calculations. Speculative decoding will almost certainly lead to incorrect results.
- Recommendation: Disable speculative decoding entirely for complex reasoning problems.

How to Manage Speculative Decoding Effectively:

Start with Enabling: Begin by enabling speculative decoding and observe the performance.
Monitor Output Carefully: Implement a system for tracking the accuracy of the model's output, especially in critical applications.
Adjust Confidence Thresholds (If Available): Some models allow you to adjust the confidence threshold used for speculative predictions. Lowering this threshold increases speed but also increases the risk of hallucinations.
Use Verification Layers: Combine speculative decoding with verification layers that check the generated output against a knowledge base or other trusted sources.

Resources for Further Learning:

OpenAI's Blog on Speculative Decoding: https://openai.com/blog/speculative-decoding
Research Papers: Search Google Scholar for "speculative decoding large language models" to find the latest research in this area.

To give you more tailored advice, could you tell me:

What kind of reasoning model are you using? (e.g., GPT-4, Claude 3, Llama 2, etc.)
What is the primary use case for the model? (e.g., chatbot, code generation, document summarization, question answering, creative writing)

The result looks correct to me. And 28 tok/s.

Testing with autocompletion

I just realized that when i test 1B, i had a 0.9 temperature. I'll have to test 1B again.

Anyway, testing 4B autocompletion. Is it better ? I'll set the temp to 0.1

topics/Gemma3-is-out.md

... Why ?

Nah there is something wrong. I blame the interaction between Continue and LM Studio here.

What about chat ?

Yeah... some weirdness here. I'll postpone the test and playing with gemma3 directly in LM Studio.

Last modified: 13 March 2025