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#open-weights

Every item tagged open-weights, newest first.

10 items

othernew1h

Does anyone have enough compute to make a distillation dataset out of GLM5.2?

A reddit user is asking if anyone has sufficient compute to create a distillation dataset from GLM-5.2, which could be used to train smaller models like Qwen-3.5. The proposed dataset would contain 700k-1M examples. This would benefit the community by enabling better training of smaller models.

Key takeaways
  • GLM-5.2 proposed as source for distillation dataset.
  • 700k-1M examples suggested for dataset size.
  • Smaller models like Qwen-3.5 could benefit from dataset.
rr/LocalLLaMA#distillation#open-weights#local-llm
modelsnew5h

Local Qwen isn't a worse Opus, it's a different tool

The Qwen 1.8B model is a local, open-weights alternative to Google's Opus, offering different strengths and use cases. While not a direct replacement, Qwen 1.8B provides a viable option for builders seeking a locally deployable model. Its performance characteristics and licensing terms make it suitable for specific applications. You can deploy Qwen 1.8B locally, giving you more control over data and infrastructure.

Key takeaways
  • Qwen 1.8B is an open-weights, locally deployable model.
  • Different performance profile compared to Opus.
  • Licensing terms allow for local deployment and customization.
HHacker News167 pts#local-llm#open-weights#model-comparison
models18h

GLM-5.2 is a win for local AI

GLM-5.2, a massive 753B MIT-licensed LLM, has been released, offering a frontier-level coding agent. Although its large footprint makes local deployment impractical for most, its open license enables community fine-tuning of smaller architectures. This could lead to significant improvements in local AI setups through distillation of GLM-5.2's reasoning and synthetic datasets.

Key takeaways
  • GLM-5.2 has a 753B parameter footprint.
  • MIT-licensed for open use.
  • Community fine-tuning of smaller models may lead to significant local AI improvements.
rr/LocalLLaMA#open-weights#local-llm#fine-tuning
models1d

GLM-5.2 just dropped open weights and it already looks weirdly strong for coding

GLM-5.2, a text-only open-weights LLM, was released with a 1M context window and MIT license. Early results show it performing well in coding tasks, near the top of arenas. Its open nature allows for local deployment and testing on real-world repositories. You can download and test GLM-5.2 locally, which may be attractive for builders seeking an alternative to API-only models.

Key takeaways
  • 1M context window, open weights, and MIT license.
  • Performs well in early coding task benchmarks.
  • Allows for local deployment and testing on real-world code.
rr/LocalLLaMA#open-weights#local-llm#coding
models1d

GLM 5.2 API is live, weights are on HF, and ollama has it already

GLM 5.2's API is now live and its model weights have been released on Hugging Face under an MIT license. The model can be run locally or accessed through existing gateways. This development allows builders to deploy GLM 5.2 without restrictions, following its initial release locked behind a paid plan.

Key takeaways
  • GLM 5.2 model weights released under MIT license on Hugging Face.
  • API is live, allowing for remote access.
  • Ollama already supports running GLM 5.2 locally.
rr/LocalLLaMA#open-weights#local-llm#api-release
models1d

GLM-5.2 is the first open-weights model to cross 80% on Terminal-Bench and beats every other open model available

GLM-5.2 is the first open-weights model to achieve over 80% on Terminal-Bench, outperforming all other open models and even Gemini. This milestone marks a significant advancement in open-weights capabilities, offering a frontier-level model at a lower cost. You can now access a highly capable model without the high costs associated with closed models.

Key takeaways
  • GLM-5.2 crosses 80% on Terminal-Bench, a first for open-weights models.
  • Beats all other open models and Gemini on benchmarks.
  • Offers frontier-level performance at a lower cost.
rr/LocalLLaMA#open-weights#benchmarks#local-llm
other1d

[Article] The Case For Open-Weight Models And Why We Can't Trust Frontier Labs | provos.org

The article argues that open-weight models are essential for ensuring AI safety and trustworthiness. It criticizes Frontier Labs for not releasing model weights, citing concerns about accountability and transparency. You should consider open-weight models for their potential to improve AI reliability and security. The discussion highlights the importance of open-weight models in the AI community.

Key takeaways
  • Open-weight models promote AI safety and trustworthiness.
  • Frontier Labs criticized for not releasing model weights.
  • Open-weight models improve AI reliability and security.
rr/LocalLLaMA#open-weights#ai-safety#transparency
modelsJun 10

DiffusionGemma

Google's experimental Gemini Diffusion model has re-emerged as an open-weight Gemma model called DiffusionGemma. The 26B parameter model is licensed under Apache 2 and is available on NVIDIA's NIM cloud API. It runs at 857 tokens/second. You can use it for free via the NIM API.

Key takeaways
  • DiffusionGemma is an open-weight, Apache 2 licensed model.
  • Runs at 857 tokens/second.
  • Available for free on NVIDIA's NIM cloud API.
SSimon Willison#open-weights#gemma#diffusion-models
modelsApr 29

Granite 4.1 LLMs: How They’re Built

IBM released Granite 4.1, a series of open-weights LLMs. The models are trained on a mix of synthetic and human-generated data. IBM used a combination of automated and human evaluation to select the best model. You can access Granite 4.1 through Hugging Face.

Key takeaways
  • Trained on synthetic and human-generated data.
  • Uses automated and human evaluation.
  • Available on Hugging Face.
HHugging Face Blog#open-source#open-weights#llms