Trained on a flavorful melange of the WizardLM, Airoboros, and Wizard Vicuna datasets. This model was trained using both linear and NTK-aware RoPE scaling in tandem. When loading, ensure that compress_pos_emb (or scale) is set to 2, and alpha_value is set to 4. Both values must be set.

Expect context length of up to 8192 to work for sure. It will probably maintain coherence into the ~12k range, but I have not tested that.

Prompt format is vicuna 1.1:

<whatever nonsense system prompt you want>
USER: ...
ASSISTANT: ...
Downloads last month
7
Inference Providers NEW
This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

Datasets used to train chargoddard/sorceroboros-33b-s2a4-gptq