chore(model gallery): add all-hands_openhands-lm-1.5b-v0.1 (#5114)
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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto 2025-04-03 10:25:46 +02:00 committed by GitHub
parent 18b320d577
commit 259ad3cfe6
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@ -5485,6 +5485,36 @@
- filename: all-hands_openhands-lm-7b-v0.1-Q4_K_M.gguf
sha256: d50031b04bbdad714c004a0dc117c18d26a026297c236cda36089c20279b2ec1
uri: huggingface://bartowski/all-hands_openhands-lm-7b-v0.1-GGUF/all-hands_openhands-lm-7b-v0.1-Q4_K_M.gguf
- !!merge <<: *qwen25
name: "all-hands_openhands-lm-1.5b-v0.1"
icon: https://github.com/All-Hands-AI/OpenHands/blob/main/docs/static/img/logo.png?raw=true
urls:
- https://huggingface.co/all-hands/openhands-lm-1.5b-v0.1
- https://huggingface.co/bartowski/all-hands_openhands-lm-1.5b-v0.1-GGUF
description: |
This is a smaller 1.5B model trained following the recipe of all-hands/openhands-lm-32b-v0.1. It is intended to be used for speculative decoding. Autonomous agents for software development are already contributing to a wide range of software development tasks. But up to this point, strong coding agents have relied on proprietary models, which means that even if you use an open-source agent like OpenHands, you are still reliant on API calls to an external service.
Today, we are excited to introduce OpenHands LM, a new open coding model that:
Is open and available on Hugging Face, so you can download it and run it locally
Is a reasonable size, 32B, so it can be run locally on hardware such as a single 3090 GPU
Achieves strong performance on software engineering tasks, including 37.2% resolve rate on SWE-Bench Verified
Read below for more details and our future plans!
What is OpenHands LM?
OpenHands LM is built on the foundation of Qwen Coder 2.5 Instruct 32B, leveraging its powerful base capabilities for coding tasks. What sets OpenHands LM apart is our specialized fine-tuning process:
We used training data generated by OpenHands itself on a diverse set of open-source repositories
Specifically, we use an RL-based framework outlined in SWE-Gym, where we set up a training environment, generate training data using an existing agent, and then fine-tune the model on examples that were resolved successfully
It features a 128K token context window, ideal for handling large codebases and long-horizon software engineering tasks
overrides:
parameters:
model: all-hands_openhands-lm-1.5b-v0.1-Q4_K_M.gguf
files:
- filename: all-hands_openhands-lm-1.5b-v0.1-Q4_K_M.gguf
sha256: 30abd7860c4eb5f2f51546389407b0064360862f64ea55cdf95f97c6e155b3c6
uri: huggingface://bartowski/all-hands_openhands-lm-1.5b-v0.1-GGUF/all-hands_openhands-lm-1.5b-v0.1-Q4_K_M.ggu
- &llama31
url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master" ## LLama3.1
icon: https://avatars.githubusercontent.com/u/153379578