Comparison
StableLM vs Awesome-LLMOps
Verdict
Pick StableLM if stableLM is a series of language models from Stability AI, featuring models like StableLM-3B-4E1T, which is a 3 billion parameter model trained on 4 trillion tokens over 4 epochs. The repository includes model updates, a; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training.
Markdown twin · StableLM alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
11views this month
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Trust & integrity
| Signal | StableLM | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (892d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- StableLM
- Stability AI Language Models
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- StableLM
- 16k
- Awesome-LLMOps
- 5.9k
Forks
- StableLM
- 1.0k
- Awesome-LLMOps
- 1.1k
Open issues
- StableLM
- 27
- Awesome-LLMOps
- 317
Language
- StableLM
- Jupyter Notebook
- Awesome-LLMOps
- Shell
Adopt for
- StableLM
- StableLM is a series of language models from Stability AI, featuring models like StableLM-3B-4E1T, which is a 3 billion parameter model trained on 4 trillion tokens over 4 epochs. The repository includes model updates, a
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- StableLM
- -
- Awesome-LLMOps
- -
Runtime
- StableLM
- -
- Awesome-LLMOps
- -
License
- StableLM
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- StableLM
- Apr 8, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- StableLM
- LLM Frameworks, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- StableLM
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- StableLM
- 892d
- Awesome-LLMOps
- 121d
Open issues (now)
- StableLM
- 27
- Awesome-LLMOps
- 317
Stars delta
- StableLM
- -6 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- StableLM
- -1 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- StableLM
- Trust report
- Awesome-LLMOps
- Trust report
Choose StableLM if…
- StableLM is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- License: StableLM is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to StableLM: checkpoints, fine-tuning, language models, pre-training.
- When you need a model that has been trained with a multi-epoch regime to study the impact of repeated tokens on performance, such as the StableLM-3B-4E1T model.
When NOT to use StableLM
- If you need a model that is strictly under the Apache-2.0 license, as StableLM models are available under different licenses like CC BY-SA-4.0 and CC BY-NC-SA-4.0.
- When you require a model that has not been trained with repeated tokens, as StableLM models like StableLM-3B-4E1T are specifically designed with this training method.
- If your project requires a model that is not a decoder-only transformer with specific architecture modifications, such as the use of Rotary Position Embeddings.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; StableLM is Jupyter Notebook.
- License: Awesome-LLMOps is CC0-1.0, StableLM is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Stability-AI/StableLM) · observed Sep 20, 2026
- GitHub forks (Stability-AI/StableLM) · observed Sep 20, 2026
- Last push (Stability-AI/StableLM) · observed Apr 8, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: StableLM 16k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between StableLM and Awesome-LLMOps?
- StableLM: Stability AI Language Models. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose StableLM over Awesome-LLMOps?
- Choose StableLM over Awesome-LLMOps when StableLM is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: StableLM is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to StableLM: checkpoints, fine-tuning, language models, pre-training; When you need a model that has been trained with a multi-epoch regime to study the impact of repeated tokens on performance, such as the StableLM-3B-4E1T model.
- When should I choose Awesome-LLMOps over StableLM?
- Choose Awesome-LLMOps over StableLM when Awesome-LLMOps is primarily Shell; StableLM is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, StableLM is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid StableLM?
- If you need a model that is strictly under the Apache-2.0 license, as StableLM models are available under different licenses like CC BY-SA-4.0 and CC BY-NC-SA-4.0. When you require a model that has not been trained with repeated tokens, as StableLM models like StableLM-3B-4E1T are specifically designed with this training method. If your project requires a model that is not a decoder-only transformer with specific architecture modifications, such as the use of Rotary Position Embeddings.
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is StableLM or Awesome-LLMOps more popular on GitHub?
- StableLM has more GitHub stars (15,678 vs 5,941). Stars measure visibility, not whether either tool fits your constraints.
- Are StableLM and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (StableLM: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to StableLM or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at StableLM alternatives and Awesome-LLMOps alternatives (StableLM markdown twin, Awesome-LLMOps markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, StableLM or Awesome-LLMOps?
- StableLM: Dormant. Awesome-LLMOps: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for StableLM and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: StableLM trust report; Awesome-LLMOps trust report.