chore(model gallery): add all-hands_openhands-lm-1.5b-v0.1 (#5114)

Signed-off-by: Ettore Di Giacinto <[email protected]>

Author: mudler

gallery/index.yaml

@@ -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