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sanchitmonga22 merged 2 commits into from
Aug 17, 2026
Merged

Add Gemma 4 and Qwen3.6/Qwen3.8 catalog rows #7

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3ba8f10
Add Gemma 4 and Qwen3.6/Qwen3.8 catalog rows
sanchitmonga22 Aug 16, 2026
7aab53f
Add Muse Glimmer 30B, Granite 4.1, and Nemotron Nano Omni catalog rows
sanchitmonga22 Aug 16, 2026
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246 changes: 238 additions & 8 deletions RunAnywhereAI/Core/Services/ModelCatalogBootstrap.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -151,6 +151,27 @@ enum ModelCatalogBootstrap {
memoryRequirement: 2_800_000_000,
supportsThinking: true
)
// Qwen3.8-27B, the newest dense Qwen release (unsloth-published GGUF,
// matching the rest of the Qwen3.x rows in this catalog).
await registerLLM(
id: "qwen3.8-27b-q4_k_m",
name: "Qwen3.8 27B Q4_K_M",
url: "https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/resolve/main/Qwen3.8-27B-Q4_K_M.gguf",
framework: .llamaCpp,
// 17,106,775,008 B of weights plus KV cache and runtime overhead.
memoryRequirement: 18_800_000_000,
supportsThinking: true
)
// Qwen3.6-35B-A3B (MoE, 35B total / 3B active, agentic-coding focused).
await registerLLM(
id: "qwen3.6-35b-a3b-q4_k_m",
name: "Qwen3.6 35B-A3B Q4_K_M",
url: "https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/resolve/main/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf",
framework: .llamaCpp,
// 22,134,528,992 B of weights plus KV cache and runtime overhead.
memoryRequirement: 24_300_000_000,
supportsThinking: true
)
// Exact P0 NVIDIA checkpoint. The pinned llama.cpp fork has native
// `nemotron` support; this exact Q4_K_M artifact was load/inference
// checked through rcli on macOS before being exposed in the catalog.
Expand Down Expand Up @@ -261,6 +282,95 @@ enum ModelCatalogBootstrap {
framework: .llamaCpp,
memoryRequirement: 2_000_000_000
)
// Gemma 4 family, text-only (unsloth GGUF, no mmproj). Distinct from
// the Gemma 4 E2B/E4B VLM rows above (`ggml-org` repo, decoder+mmproj
// pairs) — those are multimodal registrations; these are plain
// language-only chat models, hence the "-text" suffix on the E4B id to
// avoid colliding with the existing multimodal
// "gemma-4-e4b-it-q4_k_m" id at the same quant level.
await registerLLM(
id: "gemma-4-e2b-it-q4_k_m",
name: "Gemma 4 E2B IT Q4_K_M",
url: "https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF/resolve/main/gemma-4-E2B-it-Q4_K_M.gguf",
framework: .llamaCpp,
// 3,106,738,272 B of weights plus KV cache and runtime overhead.
memoryRequirement: 3_400_000_000
)
await registerLLM(
id: "gemma-4-e4b-it-text-q4_k_m",
name: "Gemma 4 E4B IT Q4_K_M",
url: "https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/resolve/main/gemma-4-E4B-it-Q4_K_M.gguf",
framework: .llamaCpp,
// 4,977,171,584 B of weights plus KV cache and runtime overhead.
memoryRequirement: 5_700_000_000
)
await registerLLM(
id: "gemma-4-12b-it-q4_k_m",
name: "Gemma 4 12B IT Q4_K_M",
url: "https://huggingface.co/unsloth/gemma-4-12b-it-GGUF/resolve/main/gemma-4-12b-it-Q4_K_M.gguf",
framework: .llamaCpp,
// 7,121,861,440 B of weights plus KV cache and runtime overhead.
memoryRequirement: 8_200_000_000
)
await registerLLM(
id: "gemma-4-26b-a4b-it-q4_k_xl",
name: "Gemma 4 26B-A4B IT Q4_K_XL",
url: "https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF/resolve/main/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf",
framework: .llamaCpp,
// 17,010,980,576 B of weights (MoE, 26B total / 4B active) plus KV
// cache and runtime overhead.
memoryRequirement: 18_700_000_000
)
// Largest dense Gemma 4. Two quants offered on purpose: Q4_K_M for
// quality, and the smaller UD-Q2_K_XL for devices that cannot fit the
// 4-bit weights.
await registerLLM(
id: "gemma-4-31b-it-q4_k_m",
name: "Gemma 4 31B IT Q4_K_M",
url: "https://huggingface.co/unsloth/gemma-4-31B-it-GGUF/resolve/main/gemma-4-31B-it-Q4_K_M.gguf",
framework: .llamaCpp,
// 18,323,733,440 B of weights plus KV cache and runtime overhead.
memoryRequirement: 20_200_000_000
)
await registerLLM(
id: "gemma-4-31b-it-q2_k_xl",
name: "Gemma 4 31B IT Q2_K_XL",
url: "https://huggingface.co/unsloth/gemma-4-31B-it-GGUF/resolve/main/gemma-4-31B-it-UD-Q2_K_XL.gguf",
framework: .llamaCpp,
// 11,774,991,296 B of weights plus KV cache and runtime overhead.
memoryRequirement: 13_500_000_000
)
// IBM Granite 4.1 family, dense, Apache 2.0 (confirmed via HF
// cardData.license). unsloth GGUF across all three sizes.
await registerLLM(
id: "granite-4.1-3b-q4_k_m",
name: "IBM Granite 4.1 3B Q4_K_M",
url: "https://huggingface.co/unsloth/granite-4.1-3b-GGUF/resolve/main/granite-4.1-3b-Q4_K_M.gguf",
framework: .llamaCpp,
// 2,099,502,400 B of weights plus KV cache and runtime overhead.
memoryRequirement: 2_400_000_000
)
await registerLLM(
id: "granite-4.1-8b-q4_k_m",
name: "IBM Granite 4.1 8B Q4_K_M",
url: "https://huggingface.co/unsloth/granite-4.1-8b-GGUF/resolve/main/granite-4.1-8b-Q4_K_M.gguf",
framework: .llamaCpp,
// 5,347,915,136 B of weights plus KV cache and runtime overhead.
// No MLX row for this size: mlx-community only has this 8B in
// bf16/nvfp4/mxfp4/mxfp8 (no clean 4-bit) plus one unofficial
// third-party "-oQ4" repo from a non-reputable quantizer.
memoryRequirement: 6_150_000_000
)
// Largest Granite 4.1, desktop-scale. Dense (verified: GGUF metadata
// reports plain `granite` architecture, no expert-routing fields).
await registerLLM(
id: "granite-4.1-30b-q4_k_m",
name: "IBM Granite 4.1 30B Q4_K_M",
url: "https://huggingface.co/unsloth/granite-4.1-30b-GGUF/resolve/main/granite-4.1-30b-Q4_K_M.gguf",
framework: .llamaCpp,
// 17,490,241,472 B of weights plus KV cache and runtime overhead.
memoryRequirement: 19_200_000_000
)
logger.info("LLM models registered")
#endif

Expand Down Expand Up @@ -494,14 +604,85 @@ enum ModelCatalogBootstrap {
memoryRequirement: 2_400_000_000,
supportsThinking: true
)
// NOTE: The MLX Gemma 4 (E2B/E4B) checkpoints are intentionally NOT
// registered. Their attention layers use an asymmetric QK-norm (some
// layers ship `self_attn.q_norm` without a matching `self_attn.k_norm`),
// but mlx-swift-lm 3.31.4's `Gemma4TextAttention` unconditionally loads
// `self_attn.k_norm.weight` and aborts with `keyNotFound` on the first
// such layer — so they download fully and then fail to load. Re-enable
// once mlx-swift-lm makes per-layer k_norm optional. The GGUF (llama.cpp)
// Gemma 4 variants below load fine and remain available.
await registerLLM(
id: "mlx-qwen3.8-27b-4bit",
name: "MLX Qwen3.8 27B 4bit",
url: "https://huggingface.co/mlx-community/Qwen3.8-27B-4bit",
framework: .mlx,
// ~16,054,541,349 B for the whole repo plus KV cache and Metal
// runtime overhead.
memoryRequirement: 17_700_000_000,
supportsThinking: true
)
await registerLLM(
id: "mlx-qwen3.6-35b-a3b-4bit",
name: "MLX Qwen3.6 35B-A3B 4bit",
url: "https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-4bit",
framework: .mlx,
// ~20,402,204,271 B for the whole repo (MoE, 35B total / 3B
// active) plus KV cache and Metal runtime overhead.
memoryRequirement: 22_400_000_000,
supportsThinking: true
)
// IBM Granite 4.1, plain dense transformer. Verified against the
// pinned mlx-swift-lm 3.31.4 checkout: both repos' config.json declare
// model_type "granite" (GraniteForCausalLM), which IS a registered
// LLMModelFactory entry (`"granite": create(GraniteConfiguration.self,
// GraniteModel.init)`), so this loads through the ordinary MLX LLM
// path. No MLX row for the 8B size — see the GGUF row's comment.
await registerLLM(
id: "mlx-granite-4.1-3b-4bit",
name: "MLX IBM Granite 4.1 3B 4bit",
url: "https://huggingface.co/mlx-community/granite-4.1-3b-4bit",
framework: .mlx,
// 2,134,391,329 B for the whole repo plus KV cache and Metal
// runtime overhead.
memoryRequirement: 2_350_000_000
)
await registerLLM(
id: "mlx-granite-4.1-30b-4bit",
name: "MLX IBM Granite 4.1 30B 4bit",
url: "https://huggingface.co/mlx-community/granite-4.1-30b-4bit",
framework: .mlx,
// 18,041,244,771 B for the whole repo plus KV cache and Metal
// runtime overhead.
memoryRequirement: 19_800_000_000
)
// NOTE: No MLX Gemma 4 checkpoint of ANY size (E2B/E4B/12B/26B-A4B/31B)
// is registered here. Their attention layers use an asymmetric QK-norm
// (some layers ship `self_attn.q_norm` without a matching
// `self_attn.k_norm`), but mlx-swift-lm 3.31.4's `Gemma4TextAttention`
// unconditionally loads `self_attn.k_norm.weight` and aborts with
// `keyNotFound` on the first such layer — so they download fully and
// then fail to load. This is architectural, not per-size, so it blocks
// every Gemma 4 MLX checkpoint until mlx-swift-lm makes per-layer
// k_norm optional. The GGUF (llama.cpp) Gemma 4 variants in the LLM
// section above load fine and remain available at every size.
//
// NOTE: No MLX row for `mlx-community/Muse-Glimmer-30B-4bit` either.
// Verified against the pinned mlx-swift-lm 3.31.4 checkout: this
// repo's config.json declares `model_type: "muse_glimmer"`, which is
// NOT among VLMModelFactory's registered types (paligemma, qwen2_vl,
// qwen2_5_vl, qwen3_vl, qwen3_5[_moe], idefics3, gemma3, gemma4[_unified],
// smolvlm, fastvlm, llava_qwen2, pixtral, mistral3, lfm2_vl, glm_ocr —
// no "muse_glimmer" anywhere) — it would download in full and then
// fail to load. Separately, this exact repo's advertised size is
// wrong: the coordinator-supplied ~19.4 GB estimate does not match
// what the HF blobs API reports for this repo (4 safetensors shards
// totaling 39,443,521,145 B, ~39.4 GB — roughly 2x), most likely
// because the vision tower and/or embeddings are kept at higher
// precision even under a nominal "4bit" LLM quant. The GGUF
// (llama.cpp) row below is unaffected and is the only Muse Glimmer
// row in this catalog.
//
// NOTE: No MLX row for
// `mlx-community/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-4bit` either,
// for the same reason: its config.json declares
// `model_type: "NemotronH_Nano_Omni_Reasoning_V3"`. VLMModelFactory
// has zero Nemotron entries of any kind, and LLMModelFactory's only
// Nemotron entry, `"nemotron_h"`, is a different, non-matching string
// (and is text-only — it has no vision path regardless). The GGUF
// (llama.cpp) row below is the only way this model is exposed here.
await registerLLM(
id: "mlx-qwen2-vl-2b-instruct-4bit",
name: "MLX Qwen2-VL 2B Instruct 4bit",
Expand Down Expand Up @@ -697,6 +878,55 @@ enum ModelCatalogBootstrap {
memoryRequirement: 3_300_000_000,
cuaProfile: RunAnywhere.CUA.faraProfile
)
// Meta Muse Glimmer 30B (Meta Superintelligence Labs, Apache 2.0,
// released 2026-08-10). Genuinely vision-capable — unsloth ships a
// real mmproj — so it registers as a VLM row like the Gemma 4 /
// Qwen2.5-VL rows above. UD-Q4_K_XL is unsloth's own top-tier dynamic
// 4-bit quant for this model; the card has no plain Q4_K_M and no
// separate "recommended" override.
await registerMultiFile(
id: "muse-glimmer-30b-q4_k_xl",
name: "Meta Muse Glimmer 30B Q4_K_XL",
files: [
("https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF/resolve/main/Muse-Glimmer-30B-UD-Q4_K_XL.gguf",
"Muse-Glimmer-30B-UD-Q4_K_XL.gguf"),
("https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF/resolve/main/mmproj-Muse-Glimmer-30B-Q8_0.gguf",
"mmproj-Muse-Glimmer-30B-Q8_0.gguf")
],
framework: .llamaCpp,
modality: .multimodal,
// 15,878,222,368 (decoder) + 2,051,685,088 (mmproj) B of weights
// plus KV cache, vision activations, and runtime overhead.
// Desktop-scale (Mac/Windows), like the largest Gemma 4/Granite
// 4.1 rows above; no phone-tier gating exists in this file today,
// so it is registered like every other heavy row and left to the
// existing HardwareTier/recommendation layer at runtime.
memoryRequirement: 20_600_000_000
)
// NVIDIA Nemotron 3 Nano Omni 30B-A3B Reasoning (MoE, 31B total / 3B
// active). This has a real mmproj (image projector), so it registers
// as a VLM row here — but ONLY image+text works through this app's
// mmproj/llama.cpp path. The model's "Omni" name markets audio/video
// understanding too; that is NOT exposed by this registration or by
// any code path in this app, so do not describe this row as full
// omni capability anywhere it is surfaced.
await registerMultiFile(
id: "nemotron-3-nano-omni-30b-a3b-reasoning-q4_k_m",
name: "NVIDIA Nemotron 3 Nano Omni 30B-A3B Reasoning Q4_K_M (Image+Text)",
files: [
("https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-Q4_K_M.gguf",
"NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-Q4_K_M.gguf"),
("https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF/resolve/main/mmproj-F16.gguf",
"mmproj-F16.gguf")
],
framework: .llamaCpp,
modality: .multimodal,
// 23,887,023,552 (decoder) + 1,587,540,224 (mmproj) B of weights
// plus KV cache, vision activations, and runtime overhead.
// Desktop-scale, same convention as Muse Glimmer above.
memoryRequirement: 28_000_000_000,
supportsThinking: true
)
logger.info("VLM models registered")
#endif

Expand Down