DeepSeek-V4-Flash-0731-MTP-JointFT-GGUF

DeepSeek V4 Flash 0731 — L37-42 Q4_K, Joint-Finetuned MTP (GGUF)

A quantized GGUF of DeepSeek V4 Flash (0731 checkpoint) with a jointly finetuned inline Multi-Token Prediction (MTP) drafter. This is the 0731 revision of DeepSeek V4 Flash. Unlike the released Flash GGUFs, this build embeds the 3-stage MTP drafter directly in the main model — no separate support/sidecar file is required. It is intended for the DwarfStar inference engine, which is the only loader that understands this layout.

File name (HF convention): DeepSeek-V4-Flash-IQ2_XXS-L37-42Q4K-MTP-JointFT-imatrix-0731.gguf

Fine-tuning data

The joint fine-tuning dataset is derived from questions posed to Fable 5, Opus 4.8, and Opus 4.7. Coding repositories from GitHub were part of the MTP drafter re-training corpus, not of this joint fine-tuning dataset.

Model details

Quantization recipe

Asymmetric mixed quantization: only the routed MoE experts are aggressively compressed; projections, routing, and output are kept high-precision to preserve quality. imatrix-tuned.

Tensor-type breakdown (from ds4 --inspect):

Inline MTP drafter (the distinguishing feature)

The 3-stage MTP drafter is embedded in the main GGUF and jointly finetuned with the main model (mtp/dspark metadata: stages=3, block=5, target_layers=40,41,42). Because the drafter ships inside the model, you do not need a separate --mtp support GGUF. DwarfStar binds it automatically at load:

ds4: embedded_mtp stages=3 bound_stages=3 source=main_model block_size=5 draft=1

Current status of MTP drafting on this build: speculative decoding is available via --mtp-draft N but is presently net-negative on this finetuned checkpoint — verification costs more than accepted drafts save (measured ~31 t/s with draft-2 vs ~37 t/s plain at 13k context, byte-identical output). The engine therefore defaults to --mtp-draft 1 (plain decoding). Drafting remains opt-in for experimentation as drafter acceptance improves.

Intended use

• Local inference of DeepSeek V4 Flash on high-memory Apple Silicon (Metal), NVIDIA CUDA, or ROCm, via the DwarfStar engine.
• Fits 128 GB unified-memory machines (96.47 GiB weights + KV/scratch).
• Research into inline/joint MTP speculative decoding.

Out of scope: use with generic GGUF loaders (llama.cpp etc.) — this file requires the DwarfStar engine's tensor layout and inline-MTP handling.

Performance (DwarfStar, Metal)

Single-run, --ctx 32768, greedy, Mac Studio M3 Ultra 512 GB:

Warm KV-cache reuse (disk checkpoint, alignment off) reduces a repeat 18k-turn prefill from ~36.8 s to ~3.4 s.

How to run

# Download (once available in antirez/deepseek-v4-gguf)
./download_model.sh q2-q4-mtp-imatrix

# Plain decoding (default, currently fastest)
./ds4 -m gguf/DeepSeek-V4-Flash-IQ2_XXS-L37-42Q4K-MTP-JointFT-imatrix-0731.gguf --temp 0

# Experiment with MTP drafting (currently net-negative, opt-in)
./ds4 -m gguf/DeepSeek-V4-Flash-IQ2_XXS-L37-42Q4K-MTP-JointFT-imatrix-0731.gguf \
      --mtp-draft 2 --temp 0

Limitations & biases

Inherits DeepSeek V4 Flash's limitations and biases. Quantization is lossy; the 2-bit routed-expert compression is imatrix-tuned to preserve quality but is not bit-exact with the FP8 base. MTP drafting is experimental and currently slower than plain decoding on this checkpoint.

License

MIT. Derived from deepseek-ai/DeepSeek-V4-Flash (MIT).

Citation

@misc{deepseekai2026deepseekv4,
      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
      author={DeepSeek-AI},
      year={2026},
      url={https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash}
}