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Gemma Coder (Fable Distilled): WHAT.. Fable LIKE OUTPUT from a 12B MODEL?
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Gemma Coder (Fable Distilled): WHAT.. Fable LIKE OUTPUT from a 12B MODEL?

Gemma 412B Agentic Fable 5 Compose: A New Local Model Review

Introduction to Gemma

  • The video introduces a new local model called "Gemma 412B Agentic Fable 5 Compose," which is trending on Hugging Face.
  • Within days of its release, the model has achieved over 6,000 downloads and approximately 200 likes.

Model Specifications and Features

  • Gemma is a fine-tuned version of Google's Gemma 412B Instruct model, optimized for coding, terminal work, tool use, reasoning, and multi-step tasks.
  • It operates locally without API costs, ensuring data privacy and offline functionality once downloaded. Released under the Apache 2.0 license.

Performance Benchmarks

  • The original Gemma scored around 15% on Tau 2 bench telecom tests; the fine-tuned version scored about 55%, indicating a significant improvement in technical agentic tasks.
  • However, it scores lower than the base model on MMLU Pro tests that measure general knowledge—indicating specialization at the cost of broader capabilities.

Recommended Usage and Setup

  • Four quantization options are available: Q3KM (5.7 GB), Q4KM (6.87 GB), Q6K (9.11 GB), and Q8 (11.8 GB). Q4KM is recommended for most users due to its balance of performance.
  • Users can set specific sampling parameters for optimal performance: temperature at one, top P at 0.95, top K at 64, and repetition penalty at 1.1 to avoid output issues.

Installation Process

  • Two installation methods are provided: using Ollama via terminal commands or LM Studio with a graphical interface for easier setup.
  • After installation through either method, users can connect Gemma to applications like Zed using an OpenAI compatible local API.

Reliability Concerns

  • Despite its potential, the model exhibits bugs that affect reliability; it may produce inconsistent outputs or fail during complex workflows.
  • Issues often stem from client configuration rather than model weights; however, this distinction may not matter much from a user experience perspective.

Practical Applications and Limitations

  • While capable in focused coding tasks or simple queries, users should be cautious when relying on it for larger projects due to potential errors in execution.
  • The creator hints at future improvements with upcoming versions aimed at enhancing stability while acknowledging current limitations in daily usage scenarios.

Conclusion

  • Overall impressions suggest that while Gemma shows promise as a free and private local coding assistant with unique capabilities, it remains too buggy for serious daily work currently. Users are encouraged to experiment but maintain realistic expectations regarding its performance.
Video description

In this video, I'll be telling you about Gemma 4 12B Agentic Fable 5 Compose, a new local model trending on Hugging Face that claims to be much better than the base Gemma model for coding, tool use, terminal workflows, and multi-step agentic tasks. -- Key Takeaways: 🚀 Gemma 4 12B Agentic Fable 5 Compose is a new local model trending on Hugging Face. 🧠 The model is fine-tuned for coding, reasoning, terminal work, tool use, and agentic workflows. 📊 It claims around a 3.5x improvement over the base Gemma 4 12B model on a local tau2-bench telecom test. 💻 You can run it locally using Ollama, LM Studio, llama.cpp, and other GGUF-compatible tools. ⚙️ The recommended quantization for most users is Q4 K M, which balances quality, speed, and memory usage. 🔗 The model can also be connected to tools like Zed through Ollama or LM Studio’s local API server. ⚠️ While the model can be surprisingly capable, it is still buggy and not reliable enough for serious daily agentic coding yet. 👍 Overall, it is an interesting local coding model worth testing, but you should keep expectations realistic and double-check its work.