chore(model gallery): add astrosage-70b

gallery/index.yaml

@@ -1275,7 +1275,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -1293,7 +1293,7 @@
   description: |
     google/gemma-3-12b-it is an open-source, state-of-the-art, lightweight, multimodal model built from the same research and technology used to create the Gemini models. It is capable of handling text and image input and generating text output. It has a large context window of 128K tokens and supports over 140 languages. The 12B variant has been fine-tuned using the instruction-tuning approach. Gemma 3 models are suitable for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes them deployable in environments with limited resources such as laptops, desktops, or your own cloud infrastructure.
   overrides:
     #mmproj: gemma-3-12b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-12b-it-Q4_K_M.gguf
   files:
@@ -1311,7 +1311,7 @@
   description: |
     Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma-3-4b-it is a 4 billion parameter model.
   overrides:
     #mmproj: gemma-3-4b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-4b-it-Q4_K_M.gguf
   files:
@@ -4447,7 +4447,7 @@
       sha256: b34dde1835752949d6b960528269af93c92fec91c61ea0534fcc73f96c1ed8b2
       uri: https://huggingface.co/ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF/resolve/main/mmproj-ultravox-v0_5-llama-3_2-1b-f16.gguf
 - &qwen25
   name: "qwen2.5-14b-instruct" ## Qwen2.5
   icon: https://avatars.githubusercontent.com/u/141221163
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
   license: apache-2.0
@@ -6408,7 +6408,7 @@
       sha256: 0fec82625f74a9a340837de7af287b1d9042e5aeb70cda2621426db99958b0af
       uri: huggingface://bartowski/Chuluun-Qwen2.5-72B-v0.08-GGUF/Chuluun-Qwen2.5-72B-v0.08-Q4_K_M.gguf
 - &smollm
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master" ## SmolLM
   name: "smollm-1.7b-instruct"
   icon: https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png
   tags:
@@ -10497,6 +10497,55 @@
     - filename: mmproj-ultravox-v0_5-llama-3_1-8b-f16.gguf
       sha256: e6395ed42124303eaa9fca934452aabce14c59d2a56fab2dda65b798442289ff
       uri: https://huggingface.co/ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF/resolve/main/mmproj-ultravox-v0_5-llama-3_1-8b-f16.gguf
+- !!merge <<: *llama31
+  name: "astrosage-70b"
+  urls:
+    - https://huggingface.co/AstroMLab/AstroSage-70B
+    - https://huggingface.co/mradermacher/AstroSage-70B-GGUF
+  description: |
+    Developed by: AstroMLab (Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal, Tuan Dung Nguyen, Alberto Accomazzi, Emily Herron, Vanessa Lama, Azton Wells, Nesar Ramachandra, Rui Pan)
+    Funded by:
+        Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility at Oak Ridge National Laboratory (U.S. Department of Energy).
+        Microsoft’s Accelerating Foundation Models Research (AFMR) program.
+        World Premier International Research Center Initiative (WPI), MEXT, Japan.
+        National Science Foundation (NSF).
+        UChicago Argonne LLC, Operator of Argonne National Laboratory (U.S. Department of Energy).
+    Reference Paper: Tijmen de Haan et al. (2025). "AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model" https://arxiv.org/abs/2505.17592
+    Model Type: Autoregressive transformer-based LLM, specialized in astronomy, astrophysics, space science, astroparticle physics, cosmology, and astronomical instrumentation.
+    Model Architecture: AstroSage-70B is a fine-tuned derivative of the Meta-Llama-3.1-70B architecture, making no architectural changes. The Llama-3.1-70B-Instruct tokenizer is also used without modification.
+    Context Length: Fine-tuned on 8192-token sequences. Base model was trained to 128k context length.
+    AstroSage-70B is a large-scale, domain-specialized language model tailored for research and education in astronomy, astrophysics, space science, cosmology, and astronomical instrumentation. It builds on the Llama-3.1-70B foundation model, enhanced through extensive continued pre-training (CPT) on a vast corpus of astronomical literature, further refined with supervised fine-tuning (SFT) on instruction-following datasets, and finally enhanced via parameter averaging (model merging) with other popular fine tunes. AstroSage-70B aims to achieve state-of-the-art performance on astronomy-specific tasks, providing researchers, students, and enthusiasts with an advanced AI assistant. This 70B parameter model represents a significant scaling up from the AstroSage-8B model. The primary enhancements from the AstroSage-8B model are:
+
+        Stronger base model, higher parameter count for increased capacity
+        Improved datasets
+        Improved learning hyperparameters
+        Reasoning capability (can be enabled or disabled at inference time)
+    Training Lineage
+        Base Model: Meta-Llama-3.1-70B.
+        Continued Pre-Training (CPT): The base model underwent 2.5 epochs of CPT (168k GPU-hours) on a specialized astronomy corpus (details below, largely inherited from AstroSage-8B) to produce AstroSage-70B-CPT. This stage imbues domain-specific knowledge and language nuances.
+        Supervised Fine-Tuning (SFT): AstroSage-70B-CPT was then fine-tuned for 0.6 epochs (13k GPU-hours) using astronomy-relevant and general-purpose instruction-following datasets, resulting in AstroSage-70B-SFT.
+        Final Mixture: The released AstroSage-70B model is created via parameter averaging / model merging:
+            DARE-TIES with rescale: true and lambda: 1.2
+            AstroSage-70B-CPT designated as the "base model"
+            70% AstroSage-70B-SFT (density 0.7)
+            15% Llama-3.1-Nemotron-70B-Instruct (density 0.5)
+            7.5% Llama-3.3-70B-Instruct (density 0.5)
+            7.5% Llama-3.1-70B-Instruct (density 0.5)
+    Intended Use: Like AstroSage-8B, this model can be used for a variety of LLM application, including
+        Providing factual information and explanations in astronomy, astrophysics, cosmology, and instrumentation.
+        Assisting with literature reviews and summarizing scientific papers.
+        Answering domain-specific questions with high accuracy.
+        Brainstorming research ideas and formulating hypotheses.
+        Assisting with programming tasks related to astronomical data analysis.
+        Serving as an educational tool for learning astronomical concepts.
+        Potentially forming the core of future agentic research assistants capable of more autonomous scientific tasks.
+  overrides:
+    parameters:
+      model: AstroSage-70B.Q4_K_M.gguf
+  files:
+    - filename: AstroSage-70B.Q4_K_M.gguf
+      sha256: 1d98dabfa001d358d9f95d2deba93a94ad8baa8839c75a0129cdb6bcf1507f38
+      uri: huggingface://mradermacher/AstroSage-70B-GGUF/AstroSage-70B.Q4_K_M.gguf
 - &deepseek
   url: "github:mudler/LocalAI/gallery/deepseek.yaml@master" ## Deepseek
   name: "deepseek-coder-v2-lite-instruct"