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**Model details:** - Parameter count: 9B (source checkpoint) - Input support: text, image (with mmproj file) - details - Decision model: yes (clef) - details - Speculative decoding: no - imatrix: no (not possible for a decision model) - details This is a decision model: it reads a state (text or JSON) together with a schema of typed questions, and returns a probability for every allowed option of every question in a single forward pass. It does not generate text. There is no chat, no completion endpoint and no prompt format to follow. llama.cpp serves it through the `/v1/systemone` endpoint of `llama-server` (the TypeSafe System One API); the request and response shapes are documented in the llama-server README. For example:

Visual QA
Input Text · Image→ Output Text
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**Model details:** - Parameter count: 27B (source checkpoint) - Input support: text, image (with mmproj file) - details - Decision model: yes (clef) - details - Speculative decoding: no - imatrix: no (not possible for a decision model) - details This is a decision model, not a text generator: it reads a state (text or JSON) together with a schema of typed questions and hands back a probability for every allowed option of every question, all in a single forward pass. It never generates text, so there's no chat, no completion endpoint, and no prompt format to worry about. llama.cpp serves it through llama-server's `/v1/systemone` endpoint (the TypeSafe System One API). The full request and response shapes are in the llama-server README, but here's what a request looks like:

Visual QA
Input Text · Image→ Output Text
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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

Text generation
Input Text→ Output Text
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Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (cognitivecomputations_Dolphin3.0-R1-Mistral-24B-Q8_0) or download them all in place (./)

Text generation
Input Text→ Output Text
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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