About
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 0.8 | |
| GGUF | IQ3_XS | 0.8 | |
| GGUF | Q3_K_S | 0.9 | |
| GGUF | IQ3_S | 0.9 | beats Q3_K* |
| GGUF | IQ3_M | 0.9 | |
| GGUF | Q3_K_M | 0.9 | lower quality |
| GGUF | Q3_K_L | 1.0 | |
| GGUF | IQ4_XS | 1.0 | |
| GGUF | Q4_K_S | 1.0 | fast, recommended |
| GGUF | Q4_K_M | 1.1 | fast, recommended |
| GGUF | Q5_K_S | 1.2 | |
| GGUF | Q5_K_M | 1.2 | |
| GGUF | Q6_K | 1.4 | very good quality |
| GGUF | Q8_0 | 1.7 | fast, best quality |
| GGUF | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
Unable to determine this model’s pipeline type. Check the
docs
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