Instructions to use Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX # Run inference directly in the terminal: llama cli -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX # Run inference directly in the terminal: llama cli -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX # Run inference directly in the terminal: ./llama-cli -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX # Run inference directly in the terminal: ./build/bin/llama-cli -hf Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
Use Docker
docker model run hf.co/Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
- LM Studio
- Jan
- Ollama
How to use Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX with Ollama:
ollama run hf.co/Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
- Unsloth Studio
How to use Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX to start chatting
- Docker Model Runner
How to use Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX with Docker Model Runner:
docker model run hf.co/Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
- Lemonade
How to use Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX
Run and chat with the model
lemonade run user.DeepSeek-V4-Flash-0731-ROCMFPX-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 3,549 Bytes
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license: mit
base_model: deepseek-ai/DeepSeek-V4-Flash-0731
tags:
- gguf
- deepseek
- rocm
- strix-halo
- quantized
---
# DeepSeek-V4-Flash-0731 — ROCmFPX (uniform baseline)
A ROCmFPX GGUF quantization of **DeepSeek-V4-Flash-0731**, built to run the full target
locally on AMD Strix Halo (Ryzen AI MAX+ 395 / Radeon 8060S) in unified memory.
**This is a baseline, deliberately.** The format assignment here is uniform by tensor role
— the same public ROCmFPX recipe, applied to the new checkpoint. It exists so that our own
adaptive-format work has an honest "before" to be measured against, on the same checkpoint,
with the same harness. It is not the interesting artifact; it is the control.
## What's in it
| role | qtype | name | block | bits/weight | tensors |
|---|---|---|---|---|---|
| attention, dense | 101 | `Q4_0_ROCMFP4_FAST` | 17 B / 32 | 4.25 | 660 |
| down projections | 104 | `Q3_0_ROCMFPX` | 14 B / 32 | 3.50 | 43 |
| gate / up projections | 107 | `Q2_0_ROCMFP2` | 10 B / 32 | 2.50 | 86 |
| passthrough (norms, embeddings, router) | — | F32 / Q6_K | — | — | 539 |
1328 tensors, single file, no companion sidecar required. The qtype histogram is an exact
match to the published preview-era ROCmFPX artifact, which is what makes it a fair control:
the format is held fixed and only the checkpoint differs.
## What it is not
- **Not the Lucebox artifact.** [Lucebox's published
ROCMFPX GGUF](https://huggingface.co/Lucebox/DeepSeek-V4-Flash-ROCMFPX) is built from the
*preview* checkpoint. This is an independent build from `0731`, matching their format.
- **Not adaptive.** No learned codebooks, no per-expert format selection. Those land in
separate repos.
- **Not imatrix-calibrated.** There is no calibration input at all: the assignment is fixed
by role in the export plan. If you are used to seeing `quantize.imatrix.*` keys in a GGUF
of this family, their absence here is correct and deliberate — see below.
## Metadata provenance
This artifact was assembled using a metadata template taken from a published GGUF of the
same family, and it initially inherited that file's `quantize.imatrix.*` keys — which
described an imatrix calibration on someone else's build machine that played no part in
producing these weights. Those keys have been removed. What remains:
```
general.name = DeepSeek-V4-Flash-0731-ROCMFPX
geoquant.source_model = deepseek-ai/DeepSeek-V4-Flash-0731
geoquant.format = ROCmFPX uniform 101/104/107
geoquant.calibration = none
```
If a GGUF's metadata claims a method that did not produce it, every downstream comparison
built on it is quietly wrong. Worth checking on any quant, not just this one.
## Speculative decode
Pair with the drafter for DSpark speculative decode:
**[DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF](https://huggingface.co/Geometric-AI/DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF)**
— extracted from this checkpoint's integrated MTP head, so it is matched to this target.
## Measurements
**Not published here yet.** Throughput, prefill, accept rate and quality for this baseline
and for the adaptive variants are being measured under one protocol on one box, and will be
reported together. A number measured here and compared against a number quoted from
somewhere else is not a comparison, so we would rather wait.
## Integrity
```
sha256 24cacd61f17bd189807d2ea51aadbde81ff15f69c4f6e358c34952bbe58dbea8
file ds4-0731-uniform.gguf (102,320,631,200 bytes)
```
## License
MIT, inherited from the base model.
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