chore(model gallery): add a-m-team_am-thinking-v1

gallery/index.yaml

@@ -757,7 +757,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -775,7 +775,7 @@
   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:
@@ -793,7 +793,7 @@
   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:
@@ -3745,7 +3745,7 @@
       sha256: b9f01bead9e163db9351af036d8d63ef479d7d48a1bb44934ead732a180f371c
       uri: huggingface://bartowski/Menlo_ReZero-v0.1-llama-3.2-3b-it-grpo-250404-GGUF/Menlo_ReZero-v0.1-llama-3.2-3b-it-grpo-250404-Q4_K_M.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
@@ -5706,7 +5706,7 @@
       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:
@@ -7282,8 +7282,32 @@
     - filename: mmproj-Qwen_Qwen2.5-VL-72B-Instruct-f16.gguf
       sha256: 6099885b9c4056e24806b616401ff2730a7354335e6f2f0eaf2a45e89c8a457c
       uri: https://huggingface.co/bartowski/Qwen_Qwen2.5-VL-72B-Instruct-GGUF/resolve/main/mmproj-Qwen_Qwen2.5-VL-72B-Instruct-f16.gguf
+- !!merge <<: *qwen25
+  name: "a-m-team_am-thinking-v1"
+  icon: https://cdn-avatars.huggingface.co/v1/production/uploads/62da53284398e21bf7f0d539/y6wX4K-P9O8B9frsxxQ6W.jpeg
+  urls:
+    - https://huggingface.co/a-m-team/AM-Thinking-v1
+    - https://huggingface.co/bartowski/a-m-team_AM-Thinking-v1-GGUF
+  description: |
+      AM-Thinking‑v1, a 32B dense language model focused on enhancing reasoning capabilities. Built on Qwen 2.5‑32B‑Base, AM-Thinking‑v1 shows strong performance on reasoning benchmarks, comparable to much larger MoE models like DeepSeek‑R1, Qwen3‑235B‑A22B, Seed1.5-Thinking, and larger dense model like Nemotron-Ultra-253B-v1.
+      benchmark
+      🧩 Why Another 32B Reasoning Model Matters?
+
+      Large Mixture‑of‑Experts (MoE) models such as DeepSeek‑R1 or Qwen3‑235B‑A22B dominate leaderboards—but they also demand clusters of high‑end GPUs. Many teams just need the best dense model that fits on a single card. AM‑Thinking‑v1 fills that gap while remaining fully based on open-source components:
+
+          Outperforms DeepSeek‑R1 on AIME’24/’25 & LiveCodeBench and approaches Qwen3‑235B‑A22B despite being 1/7‑th the parameter count.
+          Built on the publicly available Qwen 2.5‑32B‑Base, as well as the RL training queries.
+          Shows that with a well‑designed post‑training pipeline ( SFT + dual‑stage RL ) you can squeeze flagship‑level reasoning out of a 32 B dense model.
+          Deploys on one A100‑80 GB with deterministic latency—no MoE routing overhead.
+  overrides:
+    parameters:
+      model: a-m-team_AM-Thinking-v1-Q4_K_M.gguf
+  files:
+    - filename: a-m-team_AM-Thinking-v1-Q4_K_M.gguf
+      sha256: a6da6e8d330d76167c04a54eeb550668b59b613ea53af22e3b4a0c6da271e38d
+      uri: huggingface://bartowski/a-m-team_AM-Thinking-v1-GGUF/a-m-team_AM-Thinking-v1-Q4_K_M.gguf
 - &llama31
   url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master" ## LLama3.1
   icon: https://avatars.githubusercontent.com/u/153379578
   name: "meta-llama-3.1-8b-instruct"
   license: llama3.1