DeepSeek-V4-Flash-0731 284B inference in ~25 GB of RAM / Qwen3.8-Next-Flash-FP8 inference in ~18 GB of RAM on any M-series MacBook
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+10
STARS · LAST 30 DAYS
1
PER DAY
#1,453
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
Whallm gained 10 stars in the last 30 days, about 1 a day, and now has 109. It is about 1 year old and has averaged roughly 109 stars a year. It ranks #1,453 among Python repositories and #11,648 across all languages on GitHubRepo.
Trending Record
4
DAYS ON TRENDING
#9856
BEST RANK
Oct 2, 2026
FIRST APPEARANCE
Active
STATUS TODAY
Whallm has maintained a continuous presence across global trending indexes, peaking at #9856. Below is the 30-day activity profile:
💡 Overview
Whallm is an open-source project written in Python: DeepSeek-V4-Flash-0731 284B inference in ~25 GB of RAM / Qwen3.8-Next-Flash-FP8 inference in ~18 GB of RAM on any M-series MacBook.
Engineered for speed, consistency, and developer ease, it solves common hurdles in apple-silicon, deepseek-v4-flash, llm. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install Whallm⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
Whallm coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with Whallm.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install Whallm using your package manager: `pip install Whallm`
Initialize your project workspace or configuration file for Whallm.
Import Whallm into your codebase or invoke it directly from your terminal.
Execute your test suite or run `Whallm --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
yanun0323/Whallm is currently ranked #11,648 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #11,610 | hinke/the-cloud-player | JavaScript | ★ 110 | Compare ↗ |
| #11,610 | dan-manges/unit-record | Ruby | ★ 110 | Compare ↗ |
| #11,610 | League-of-Fabulous-Developers/FoundryVTT-Fabula-Ultima | JavaScript | ★ 110 | Compare ↗ |
| #11,610 | ThinkFlowLab/system1-agents | Python | ★ 110 | Compare ↗ |
| #11,610 | mockzilla/mockzilla | Go | ★ 110 | Compare ↗ |
| #11,648 | yanun0323/Whallm This Project | Python | ★ 109 | |
| #11,648 | Vauth/FoxyVPN | Kotlin | ★ 109 | Compare ↗ |
| #11,648 | boadij/pi-herdsman | TypeScript | ★ 109 | Compare ↗ |
| #11,648 | graygnatconsole/mcp-audit-tool | Python | ★ 109 | Compare ↗ |
| #11,648 | kveld9/kveld-morphe-patches | Kotlin | ★ 109 | Compare ↗ |
| #11,648 | telmich/gpm | C | ★ 109 | Compare ↗ |
Frequently Asked Questions
What does Whallm do? +
DeepSeek-V4-Flash-0731 284B inference in ~25 GB of RAM / Qwen3.8-Next-Flash-FP8 inference in ~18 GB of RAM on any M-series MacBook
What language is Whallm written in? +
The primary language is Python. Topics include: apple-silicon, deepseek-v4-flash, llm, llm-inference, local-ai.
Is Whallm actively maintained? +
Yes, the last recorded push was on Oct 2, 2026 with 0 open issues being tracked.
How many stars does Whallm have? +
Whallm has 109 stars and 13 forks on GitHub.
How does Whallm rank among GitHub repositories? +
With 109 stars, yanun0323/Whallm is ranked #11,648 globally across all repositories tracked on GitHubRepo and #1,453 among Python projects.
What license is Whallm distributed under? +
The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.
