Noris Provider: Available Models

As of 2026-06-27, the noris provider at https://ai.noris.de/v1 exposes these models for the Schön Consulting virtual key:

Anthropic Models (vision-capable)

anthropic/claude-fable-5
anthropic/claude-haiku-4-5-20251001 — ✅ fast, vision-capable, good for auxiliary.vision
anthropic/claude-opus-4-1-20250805
anthropic/claude-opus-4-5-20251101
anthropic/claude-opus-4-6
anthropic/claude-opus-4-7
anthropic/claude-opus-4-8
anthropic/claude-sonnet-4-5-20250929
anthropic/claude-sonnet-4-6

Self-hosted vLLM Models

vllm/gemma-4-31b-it — usable (nutrition-coach uses this)
vllm/gemma-4-31b-it-dynamo
vllm/glm-5-2-nvfp4 — ✅ verified working (2026-06-27). Current CEO/default model.
vllm/gpt-oss-120b — usable
vllm/harrier-oss-v1-0.6b
vllm/moonshotai/kimi-k2.6 — usable (infra-sre uses this)
vllm/qwen3.5-122b-a10b
vllm/qwen3.6-27b-nvfp4 — ✅ verified working (2026-06-18). Returns reasoning-only format: content: null, reasoning: "..."
vllm/qwen3.6-35b-a3b

Vision / Multimodal Capability (tested 2026-06-27)

Tested by sending image+text to /v1/chat/completions:

Model	Vision?	Notes
`vllm/gemma-4-31b-it`	✅ Yes	Good descriptions, recommended for `auxiliary.vision`
`vllm/qwen3.5-122b-a10b`	✅ Yes	Works but terse output
`vllm/glm-5-2-nvfp4`	❌ No	Returns `"glm-5-2-nvfp4 is not a multimodal model"`
`vllm/qwen3.6-27b-nvfp4`	❌ No	HTTP 400
All `anthropic/*` models	✅ Yes	`claude-haiku-4-5` fastest option

User preference: prefer vllm/ models over anthropic/ for auxiliary tasks.

Current auxiliary.vision config:

hermes config set auxiliary.vision.provider noris
hermes config set auxiliary.vision.model vllm/gemma-4-31b-it
hermes config set auxiliary.vision.base_url ''
hermes config set auxiliary.vision.api_key ''

Testing vision capability programmatically

import yaml, urllib.request, json, base64

with open("/home/debian/.hermes/config.yaml") as f:
    cfg = yaml.safe_load(f)
key = cfg["providers"]["noris"]["api_key"]

with open("test_image.jpg", "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode()

payload = {
    "model": "<MODEL_NAME>",
    "messages": [{"role": "user", "content": [
        {"type": "text", "text": "Describe this image briefly."},
        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}}
    ]}],
    "max_tokens": 50
}
req = urllib.request.Request(
    "https://ai.noris.de/v1/chat/completions",
    data=json.dumps(payload).encode(),
    headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"}
)
try:
    with urllib.request.urlopen(req, timeout=30) as resp:
        print(json.loads(resp.read())["choices"][0]["message"]["content"])
except Exception as e:
    print(f"Not multimodal: {e}")

Embedding / Reranker Models (non-chat)

vllm/bge-reranker-v2-m3
vllm/jina-reranker-v2-base-multilingual

Blocked Models

vllm/gemma-4-27b-it — returns 403: {"type":"model_blocked","error":{"message":"Model 'gemma-4-27b-it' is not allowed for this virtual key"}}

Verification

import yaml, urllib.request, json

with open("/home/debian/.hermes/config.yaml") as f:
    cfg = yaml.safe_load(f)
key = cfg["providers"]["noris"]["api_key"]

req = urllib.request.Request(
    "https://ai.noris.de/v1/models",
    headers={"Authorization": f"Bearer {key}"}
)
with urllib.request.urlopen(req) as resp:
    models = json.loads(resp.read())
for m in sorted(models["data"], key=lambda x: x["id"]):
    print(m["id"])

Always verify model availability via the /v1/models endpoint before assigning to an agent profile. A blocked model will cause silent 403 errors on first user interaction.

Vision pitfall: Not all chat models support image input. vllm/glm-5-2-nvfp4 (the default CEO model) is NOT multimodal — vision_analyze will fail with "is not a multimodal model". Set auxiliary.vision.model to a vision-capable model like vllm/gemma-4-31b-it. See the "Vision / Multimodal Capability" section above for the full tested matrix.

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