--- license: mit library_name: gguf pipeline_tag: text-generation base_model: deepseek-ai/DeepSeek-V4-Flash base_model_relation: quantized quantized_by: antirez language: - en tags: - gguf - quantized - deepseek - deepseek-v4 - deepseek-v4-flash - moe - mixture-of-experts - 2-bit - 4-bit - iq2_xxs - q2_k - q4_k - ds4 - apple-silicon - metal --- # DeepSeek V4 Flash — GGUF for ds4 This quants are specific for the DS4 inference engine. They may work with other inference engines or not (they should, but not the MTP model which requires a specific loader). https://github.com/antirez/ds4 ## Files | File | Size | Routed experts (`ffn_{gate,up,down}_exps`) | Everything else | |---|---:|---|---| | `DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2.gguf` | 80.8 GiB | `IQ2_XXS` (gate, up) + `Q2_K` (down) | `Q8_0` attn proj / shared experts / output, `F16` router + embed + indexer + compressor + HC, `F32` norms / sinks / bias | | `DeepSeek-V4-Flash-Q4KExperts-F16HC-F16Compressor-F16Indexer-Q8Attn-Q8Shared-Q8Out-chat-v2.gguf` | 153.3 GiB | `Q4_K` (all three) | same as above | | `DeepSeek-V4-Flash-MTP-Q4K-Q8_0-F32.gguf` | 3.6 GiB | MTP / speculative-decoding support (optional, not standalone). | | Use **q2** on 128 GB Mac machines, **q4** on machines with ≥ 256 GB RAM, pair either with **MTP** for optional speculative decoding. ## Quantization recipe The filename is the spec. In detail, for the **q2** file: | Tensor class | Quant | Notes | |---|---|---| | `blk.*.ffn_gate_exps`, `blk.*.ffn_up_exps` | **`IQ2_XXS`** | routed-expert up/gate | | `blk.*.ffn_down_exps` | **`Q2_K`** | routed-expert down (K-quant for quality) | | `blk.*.ffn_{gate,up,down}_shexp` | `Q8_0` | shared experts | | `blk.*.attn_q_a`, `attn_q_b`, `attn_kv`, `attn_output_a`, `attn_output_b` | `Q8_0` | all attention projections (MLA + low-rank output) | | `output.weight` | `Q8_0` | output head | | `token_embd.weight` | `F16` | input embedding | | `blk.*.ffn_gate_inp` (router) | `F16` | learned router | | `blk.*.exp_probs_b` (router bias), `blk.*.attn_sinks`, all `*_norm.weight` | `F32` | | | `blk.*.ffn_gate_tid2eid` | `I32` | hash-routing tables (first 3 layers only) | | `blk.*.attn_compressor_*`, `blk.*.indexer_*`, `blk.*.hc_*`, `blk.*.output_hc_*` | `F16` / `F32` | DSv4-specific auxiliary blocks | For the **q4** file, only the three routed-expert classes change to `Q4_K`. Everything else is byte-for-byte identical to the q2 recipe. The motivation behind the asymmetry: the routed experts are the majority of the parameter count but each individual expert handles only a fraction of tokens, so aggressive quantization on them costs less in average quality than the same treatment of router, projections, or shared experts. Keeping the decision-making components at `Q8_0` preserves model behavior; crushing the experts buys the size. ## Usage ```bash git clone https://github.com/antirez/ds4 cd ds4 ./download_model.sh q2 # 128 GB RAM machines ./download_model.sh q4 # >= 256 GB RAM machines ./download_model.sh mtp # optional MTP / speculative decoding make ./ds4 -p "Explain Redis streams in one paragraph." ./ds4-server --ctx 100000 --kv-disk-dir /tmp/ds4-kv --kv-disk-space-mb 8192 ``` The `download_model.sh` script fetches from this repository, resumes partial downloads, and points `./ds4flash.gguf` at the selected variant. ## License MIT. The base model copyright is held by DeepSeek; the GGUFs are redistributed under the base model's release terms.
