Choose models

You don’t have to guess which model to run. hal0 ships a curated catalogue — a hand-picked list of good defaults, each pointing at a specific GGUF (or safetensors) file in a Hugging Face repo, sized for the target’s unified-memory pool. Pull any of them by id:

hal0 model pull qwen3.6-27b

The catalogue ships with each release (it lives in code, not a remote manifest), so you can never land on a stale pick list.

The Models view renders each curated entry as a card with size, license, and minimum-memory badges.

Chat picks

These are surfaced first by the FirstRun wizard, ordered roughly largest to smallest. The headline picks are quantized to UD-Q4_K_XL / Q5_K_XL for the quality margin a large unified pool affords.

Id	Model	Size	Min memory	License	Notes
qwen3-coder-next	Qwen3 Coder Next	~49 GB	~56 GB	Apache-2.0	Frontier coding model; needs the full pool.
qwen3.6-27b	Qwen3.6 27B (MTP ROCmFP4)	~17 GB	~22 GB	other (see HF repo)	General-purpose default with MTP and vision support; ROCmFP4 build for Strix Halo.
gpt-oss-20b	GPT-OSS 20B	~12 GB	~16 GB	Apache-2.0	OpenAI open-weights 20B.
qwen3.5-9b	Qwen3.5 9B	~6 GB	~8 GB	Apache-2.0	Lean default; fits alongside embed + voice slots.
qwen3.5-0.8b	Qwen3.5 0.8B	~0.6 GB	~1 GB	Apache-2.0	Tiny; sub-second cold start. Doubles as the install smoke test.
qwen3-4b	Qwen3 4B Instruct	~2.5 GB	~4 GB	Apache-2.0	Fast all-rounder for a 4–8 GB budget.
llama32-3b	Llama 3.2 3B Instruct	~2 GB	~3 GB	Llama-3.2-Community	Small and fast; good for low-memory hosts.
phi3-mini	Phi-3 Mini 4K Instruct	~2.4 GB	~3 GB	MIT	Compact reasoning, MIT-licensed.

Tip

qwen3.5-0.8b is the recommended first pull on a fresh install — it verifies the whole slot lifecycle (pull → load → serve) in seconds before you commit to downloading a 20+ GB pick.

Embed and rerank picks

llama.cpp-compatible GGUFs that fan out to gpu-vulkan / gpu-rocm / cpu. Assign these to an embedding or reranking slot.

Id	Model	Capability	Size	License
nomic-embed-text-v1.5-q8_0	Nomic Embed Text v1.5 (Q8_0)	embed	~0.15 GB	Apache-2.0
bge-base-en-v1.5-q4_k_m	BGE Base EN v1.5 (Q4_K_M)	embed	~0.07 GB	MIT
bge-reranker-base-q4_k_m	BGE Reranker Base (Q4_K_M)	rerank	~0.26 GB	MIT
bge-reranker-v2-m3-q4_k_m	BGE Reranker v2 M3 (Q4_K_M)	rerank	~0.44 GB	Apache-2.0

Note

Embedding quality is brittle under aggressive quantization, so the nomic default ships at Q8_0 — the size delta is negligible on a unified pool. Rerank slots must be created with --type reranking (see Manage slots).

Image picks

Routed through ComfyUI — these land in ComfyUI’s models/checkpoints tree rather than the per-id model directory, so its loaders pick them up directly.

Id	Model	Size	License
sdxl-turbo	SDXL Turbo	~6.5 GB	SAI-NC-Research-Community (research only)
sdxl-lightning	SDXL Lightning (8-step)	~6.8 GB	CreativeML-OpenRAIL++-M
sd-1.5-pruned-emaonly	Stable Diffusion 1.5	~4.3 GB	CreativeML-Open-RAIL-M

Caution

Image picks span the licensing spectrum on purpose — SDXL Turbo is research-only, SD 1.5 is RAIL-M. Read the license before using output commercially.

How model fit is evaluated

Before a model is assigned to a slot, hal0 computes a fit verdict — allowed, degraded, or blocked — with stable reason strings the dashboard renders as chips. The check runs three gates in order:

1. Type match. The model’s classified modality must match the slot type. A chat model maps to an llm slot, embed → embedding, rerank → reranking, stt → transcription, tts → tts, image → image. A mismatch is blocked (model.slot_type_mismatch).

2. Resolvability. The model id must resolve — present in the local registry. An unresolvable id is blocked (model.not_resolvable).

3. Profile + device class. The slot’s profile must support the slot type, and the device must agree with the profile’s device class (gpu-rocm/gpu-vulkan → gpu, cpu → cpu, npu → npu). NPU and image mismatches are hard blocked failures — they route to different runtime families. A GPU↔CPU mismatch is degraded instead: a custom image may still run, but it needs operator attention before launch.

A degraded verdict is allowed to proceed; only blocked stops the assignment. This is why the device you pick for a slot has to be consistent with its profile — see Manage slots for the --hardware flag that sets it.

Off-catalogue models

Anything not in the catalogue still works — search Hugging Face, inspect a repo’s variants, and pull by coordinates. See Pull and register models.