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SO

MakazhanAlpamys/Soup

Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.

1.7k Stars263 Forks88 Open Issues1.7k WatchersPythonApache-2.0
LLM ToolDeveloper Tool
Review Readiness

This repository page is useful for visitors, but Git-Stars keeps it out of search indexing until more original evidence and comparison context are available.

70

review score

Needs original analysis
Decision Snapshot

Problem solved

It solves the pain of LLM fine-tuning infrastructure, such as SSHing into GPU boxes, complex configs, and high VRAM requirements, by providing an automated, one-command workflow that works locally on modest hardware.

Deployment reality

The available setup signal starts with: pip install "soup-cli[train]". Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.

Commercial use

The recorded license is Apache-2.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.

Capability ceiling

MakazhanAlpamys/Soup still needs a clearer capability analysis. Use the metadata as a discovery signal, not as a production recommendation.

Source and compliance noteLast synced: Aug 16, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy
Editorial Evaluation

MakazhanAlpamys/Soup is tracked as a Python project in the LLM Tool, Developer Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.

Momentum check: the repository has 1.7k total stars, with +297 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.

Maintenance check: current activity is Active; the latest push was 7 days ago, and the open issue queue is 88, about 5.29% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.

Adoption check: 263 forks and 1.7k watchers suggest how often the project is reused or followed. License signal: Apache-2.0. Always verify license compatibility before commercial or internal use.

Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.

Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 297. Follow the original GitHub repository for final install, security, and release information.

Evidence Checklist

Analysis

Limited

Needs stronger original analysis before indexing.

Sources

Ready

Repository metadata and README/source references are attached.

License

Ready

Apache-2.0 is recorded for review.

Maintenance

Ready

Recent activity is visible in repository metadata.

Alternatives

Ready

Enough nearby projects exist for comparison.

Best For
  • AI prototypes, LLM workflows, and agent-style applications
  • developer workflow automation and command-line tooling
  • Python teams evaluating ecosystem-native tooling
  • use cases where recent maintenance matters
Avoid When
  • you need a legal review, security audit, or production SLA
Adoption Signals

Momentum

1.7k Stars

Reuse

263 Forks

Attention

1.7k Watchers

Maintenance

active

License

Apache-2.0

Open issues

88

Overview

Soup is a CLI tool that simplifies fine-tuning and post-training of large language models (LLMs) with a single command, eliminating the need for SSH or complex configuration. It supports local training on consumer GPUs via QLoRA and layer streaming, enabling fine-tuning of 8B models on as little as 4 GB VRAM.

Key Features

- Zero SSH: no need to manage remote GPU servers. - One config: simple YAML file for all training settings. - Auto everything: automatic batch size, GPU detection, and quantization. - Layer streaming (BETA): enables fine-tuning 8B models on 4 GB GPUs by streaming frozen layers. - Release gate with noise floor: `soup ship` evaluates model improvements with statistical significance.

Tool Positioning

LLM Tool

Libraries and tools for LLM apps, RAG, prompts, and evals

Developer Tool

Tools that improve coding, testing, build, and local workflow

Quick Start
pip install "soup-cli[train]"
View on GitHub Project Homepage
Project Activity

73

Health Score

Active

Commit Activity

Feb 20, 2026

Created

Aug 15, 2026

Last push

Source Trail

GitHub repository metadata

metadata

GitHub README

readme_summary

Star History

+0

Today's growth

+0

7-day growth

+0

30-day growth

Aug 16, 2026Aug 16, 2026
Community Health
263

Forks

88

Open

1.7k

Watchers

Owner
SO

MakazhanAlpamys

GitHub profile
Topics & Language
Pythoncliconsumer-gpudpofine-tuningggufhuggingfacellmllmopslocal-ailocal-llmloralow-vrammachine-learningollamapeftpythonpytorchqlorasfttransformers
Ecosystem & Usage
GitHub Repository Project Website
Alternatives & Comparison

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247k

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240k
License
Apache-2.0
CreatedFeb 20, 2026
Last pushAug 15, 2026
Last syncedAug 16, 2026
Repository Standards

✓

License

✓

Forked

✓ Active

Maintained