Instructions to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 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 kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

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 kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

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 kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Ollama
How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with Ollama:
```
ollama run hf.co/kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 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 kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 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 kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 to start chatting

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with Docker Model Runner:
```
docker model run hf.co/kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-0731-ROCmFP4-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4:Q4_0_ROCMFP" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

DeepSeek-V4-Flash-0731-ROCmFP4 / README.md

kingjones777

card: document both quants (Q3 + Q4_0_ROCMFP4_STRIX)

1fd6eb6 verified 3 days ago

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	---
	license: other
	license_name: deepseek
	license_link: https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-MODEL
	base_model: deepseek-ai/DeepSeek-V4-Flash-0731
	base_model_relation: quantized
	quantized_by: kingjones777
	pipeline_tag: text-generation
	library_name: gguf
	language:
	- en
	tags:
	- deepseek
	- deepseek-v4
	- rocmfp4
	- rocmfpx
	- strix-halo
	- gfx1151
	- amd
	- vulkan
	- gguf
	- moe
	- rocm
	- ryzen-ai-max
	---

	### Quants in this repo

	\| build \| size \| notes \|
	\|---\|---\|---\|
	\| `...-Q3-ROCmFP4-` (4 shards) \| 100.45 GiB* \| lower-bit build \|
	\| `...-Q4_0_ROCMFP4_STRIX-` (4 shards) \| 140.98 GiB* \| ftype 105 `STRIX` — Strix Halo attn-K/V recipe \|

	Both target AMD gfx1151 (Ryzen AI MAX+ 395 / Strix Halo) and require a llama.cpp with the
	`Q4_0_ROCMFP4_*` quant types — see [`charlie12345/ROCmFPX`](https://github.com/charlie12345/ROCmFPX).

	⚠️ Note on the STRIX (105) tier: it protects attention K/V but not the LM head. On
	large-vocabulary models we now prefer tier 102 `COHERENT` plus explicit
	`--output-tensor-type q6_K --token-embedding-type q6_K`. This STRIX build predates that finding.


	# DeepSeek-V4-Flash-0731 — ROCmFP4 (Strix Halo) GGUF

	This is a ROCmFP4 quant of `deepseek-ai/DeepSeek-V4-Flash-0731`, built to fit a single AMD Strix Halo box (128 GB unified memory) with full GPU offload. As far as I can tell it's the first ROCmFP4 quant of this model. I made it with the [ROCmFPX fork](https://github.com/charlie12345/ROCmFPX) of `llama.cpp` for the `gfx1151` (Radeon 8060S / Ryzen AI MAX+ 395) Vulkan/ROCm stack.

	\| \| \|
	\|---\|---\|
	\| Base model \| [`deepseek-ai/DeepSeek-V4-Flash-0731`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) \|
	\| Quant \| Q3 — mixed ROCmFP4, experts ~3.14 bpw, 2.92 BPW overall \|
	\| Size \| ~101 GB (fits 128 GB unified memory with headroom) \|
	\| Arch \| `deepseek4` (sparse MoE, 256 experts, indexer/DSA attention) \|
	\| Target HW \| AMD Strix Halo `gfx1151` iGPU (Ryzen AI MAX+ 395), Vulkan RADV \|
	\| Loader \| ROCmFPX fork — stock `llama.cpp` cannot load ROCmFP4 tensors \|

	## Why I made it

	A standard 4-bit GGUF of this model comes out around 141 GB, which overflows a 128 GB Strix Halo's shared pool and spills to CPU. I wanted the largest-quality quant that still fully offloads on a single box and stays coherent, so I mixed the expert tensors down to land it at ~101 GB.

	Recipe (quantized from the F16 with the fork's `llama-quantize`):

	- base type `Q2_0_ROCMFPX`
	- `ffn_down_exps` → `q3_0_rocmfpx` (3.5 bpw)
	- `ffn_gate_exps`, `ffn_up_exps` → `q2_0_rocmfpx` (2.5 bpw)
	- attention / embeddings → ROCmFPX; norms kept in fp32

	The ROCmFP4 (`_ROCMFPX`) types hold quality better than equivalent-bit k-quants on this hardware while using the FP4 paths on `gfx1151`.

	## Running it

	Build the [ROCmFPX fork](https://github.com/charlie12345/ROCmFPX) (`llama-server` / `llama-cli`) for `gfx1151`, then:

	```bash
	export HSA_OVERRIDE_GFX_VERSION=11.5.1 GGML_HIP_ENABLE_UNIFIED_MEMORY=1
	export AMD_VULKAN_ICD=RADV VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/radeon_icd.json

	./llama-server \
	-m DeepSeek-V4-Flash-0731-Q3-ROCmFP4-00001-of-00004.gguf \
	-dev Vulkan0 -ngl 999 -fa on -fit off --no-mmap \
	-c 8192 -n 2048 -np 1 -b 1024 -ub 512 -t 16 --poll 50 --jinja \
	--reasoning-format deepseek \
	--chat-template-kwargs '{"enable_thinking":false}' \
	--host 0.0.0.0 --port 8084
	```

	Notes from getting it stable on my box:

	- `-fit off` — the fork's auto-fit step crashed on this arch for me; pin `-ngl 999` and turn it off.
	- `--no-mmap` — important for MoE speed. With mmap, experts page-fault per token and throughput roughly halves.
	- `-c 8192` with `-b 1024 -ub 512` keeps the graph pool under its limit; larger context can overflow it.
	- `-n 2048` caps runaway generations so one request can't hold the single slot forever.
	- `--chat-template-kwargs '{"enable_thinking":false}'` gives fast, direct answers. Drop it (or pass `enable_thinking:true` per request) for the model's reasoning mode.
	- Expect roughly 5–8 tok/s — it's a 101 GB model on one iGPU. Use streaming for a usable feel.

	## A note on MTP

	This checkpoint ships a multi-token-prediction (nextn) head, and I kept those tensors in this quant. I got a working MTP inference path running on this arch and tested it thoroughly, but on this hardware/loader combination MTP nets out slightly slower than plain decoding — the draft head's acceptance is low and the sparse-MoE verify step can't amortize its weight reads across draft tokens. I ran it against draft depth, the probability threshold, and draft-head precision; none of them turned it into a win here. So I ship it with MTP off. If you want the model's advertised MTP speedup, run it on a CUDA/vLLM stack instead of this one.

	## License

	Derived from `deepseek-ai/DeepSeek-V4-Flash-0731`; the original model's license applies (see `license_link`). This upload is only a quantization — all capabilities and limitations are the base model's.

	<!-- PEER-TABLE:START -->

	## Other public builds of this model

	Compiled from Hugging Face repository metadata — file sizes, shipped files, quant variant as named by each repo. No third-party build was run or benchmarked here, so this table makes no speed or quality claim about any of them. It is here so you can see the size and format options at a glance and pick what fits your hardware.

	\| Repository \| Largest model file \| Variant \| Ships \| Downloads \| Likes \|
	\| --- \| ---: \| --- \| --- \| ---: \| ---: \|
	\| [`drowzeys/keys-DeepSeekV4-Flash-GA-0731-Dspark-Abliterated-32-32`](https://huggingface.co/drowzeys/keys-DeepSeekV4-Flash-GA-0731-Dspark-Abliterated-32-32) \| 3.44 GiB \| — \| safetensors \| 2144 \| 73 \|
	\| [`dealignai/DeepSeek-V4-Flash-0731-CRACK-NVFP4`](https://huggingface.co/dealignai/DeepSeek-V4-Flash-0731-CRACK-NVFP4) \| 3.44 GiB \| NVFP4 \| safetensors \| 0 \| 1 \|
	\| [`auroter/DeepSeek-V4-Flash-0731-NVFP4`](https://huggingface.co/auroter/DeepSeek-V4-Flash-0731-NVFP4) \| 3.54 GiB \| NVFP4 \| safetensors \| 1504 \| 3 \|
	\| [`sakamakismile/DeepSeek-V4-Flash-0731-Abliterated-NVFP4`](https://huggingface.co/sakamakismile/DeepSeek-V4-Flash-0731-Abliterated-NVFP4) \| 3.54 GiB \| NVFP4 \| safetensors \| 952 \| 11 \|
	\| [`mmangkad/DeepSeek-V4-Flash-0731-NVFP4`](https://huggingface.co/mmangkad/DeepSeek-V4-Flash-0731-NVFP4) \| 3.54 GiB \| NVFP4 \| safetensors \| 300 \| 1 \|
	\| [`nmitchko/DeepSeek-V4-Flash-0731-Latent-Reasoning`](https://huggingface.co/nmitchko/DeepSeek-V4-Flash-0731-Latent-Reasoning) \| 3.54 GiB \| — \| safetensors \| 196 \| 4 \|
	\| [`MJPansa/DeepSeek-V4-Flash-0731-NVFP4`](https://huggingface.co/MJPansa/DeepSeek-V4-Flash-0731-NVFP4) \| 3.54 GiB \| NVFP4 \| safetensors \| 110233 \| 10 \|
	\| [`Rarri/DeepSeek-V4-Flash-0731-NVFP4`](https://huggingface.co/Rarri/DeepSeek-V4-Flash-0731-NVFP4) \| 3.63 GiB \| NVFP4 \| safetensors \| 208 \| 3 \|
	\| [`Ralii/DeepSeek-V4-Flash-0731-DSpark-Selective-Q4_K_M-GGUF`](https://huggingface.co/Ralii/DeepSeek-V4-Flash-0731-DSpark-Selective-Q4_K_M-GGUF) \| 9.85 GiB \| — \| single model file \| 23 \| 0 \|
	\| [`kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4`](https://huggingface.co/kingjones777/DeepSeek-V4-Flash-0731-ROCmFP4) (this repo) \| 31.66 GiB \| ROCmFP4 \| 4 model files \| 217 \| 1 \|
	\| [`bullerwins/keys-DeepSeekV4-Flash-GA-0731-Dspark-Abliterated-32-32-GGUF`](https://huggingface.co/bullerwins/keys-DeepSeekV4-Flash-GA-0731-Dspark-Abliterated-32-32-GGUF) \| 45.70 GiB \| — \| 5 model files \| 2344 \| 4 \|
	\| [`julianmb/DeepSeek-V4-Flash-0731-IQ2XXS-STRIX`](https://huggingface.co/julianmb/DeepSeek-V4-Flash-0731-IQ2XXS-STRIX) \| 80.76 GiB \| STRIX \| single model file \| 423 \| 1 \|
	\| [`otheru/DeepSeek-V4-Flash-Strix-Halo-GGUF`](https://huggingface.co/otheru/DeepSeek-V4-Flash-Strix-Halo-GGUF) \| 85.26 GiB \| STRIX \| drafter \| 2667 \| 12 \|
	\| [`Lucebox/DeepSeek-V4-Flash-0731-ROCmFP3`](https://huggingface.co/Lucebox/DeepSeek-V4-Flash-0731-ROCmFP3) \| 95.29 GiB \| ROCmFP3 \| 2 model files \| 5372 \| 14 \|
	\| [`Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX`](https://huggingface.co/Geometric-AI/DeepSeek-V4-Flash-0731-ROCMFPX) \| 95.29 GiB \| ROCmFPX \| single model file \| 50 \| 3 \|
	\| [`Geometric-AI/DeepSeek-V4-Flash-0731-ROCmFP3-MIX`](https://huggingface.co/Geometric-AI/DeepSeek-V4-Flash-0731-ROCmFP3-MIX) \| 95.29 GiB \| ROCmFP3 \| 2 model files \| 1551 \| 4 \|
	\| [`Kevletesteur/DeepSeek-V4-Flash-0731-StrixHalo-Verified-GGUF`](https://huggingface.co/Kevletesteur/DeepSeek-V4-Flash-0731-StrixHalo-Verified-GGUF) \| 96.07 GiB \| STRIX \| single model file \| 51 \| 1 \|
	\| [`bertholomus/DeepSeek-V4-Flash-0731-DSpark-Graph8`](https://huggingface.co/bertholomus/DeepSeek-V4-Flash-0731-DSpark-Graph8) \| — \| — \| single model file \| 0 \| 4 \|
	\| [`JasonW2025/DeepSeek-V4-Flash-0731-CB-C16-NVFP4`](https://huggingface.co/JasonW2025/DeepSeek-V4-Flash-0731-CB-C16-NVFP4) \| — \| NVFP4 \| single model file \| 0 \| 3 \|
	\| [`bertholomus/DeepSeek-V4-Flash-0731-DSpark-Graph8-4xGB10`](https://huggingface.co/bertholomus/DeepSeek-V4-Flash-0731-DSpark-Graph8-4xGB10) \| — \| — \| single model file \| 0 \| 1 \|

	Base model: [`deepseek-ai/DeepSeek-V4-Flash-0731`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731). Generated from Hub metadata; download counts move over time.

	<!-- PEER-TABLE:END -->

	<!-- CREDITS:START -->

	## Acknowledgements

	This build would not exist without the work below. Please star and follow these
	projects — the quantisation format used here is their engineering, not mine.

	**[ROCmFPX](https://github.com/charlie12345/ROCmFPX) — maintained by
	[`charlie12345`](https://github.com/charlie12345) / `caf`**
	The ROCmFP4 / ROCmFPX tensor formats (ggml types 100–106) exist only in this fork.
	Every ROCmFP4 file in this repository was produced with its `llama-quantize`, and
	runs on its runtime. The fork also credits collaborators ciru-ai, Tom Turney,
	PlunderStruck and Aydan S., and acknowledges AMD for hardware support.
	Licensed MIT, based on upstream llama.cpp.

	[llama.cpp](https://github.com/ggml-org/llama.cpp) — ggml-org and contributors
	The inference engine, GGUF format and conversion tooling everything here is built on.

	[AMD ROCm](https://github.com/ROCm/ROCm)
	The compute platform these builds target — ROCm 7.2.4 on gfx1151 / Radeon 8060S.

	Base model authors — see `base_model` in the metadata above; all model weights,
	licences and capabilities are theirs. This repository contributes quantisation and
	measurement only.

	If you use these files, please credit ROCmFPX alongside this repository.

	<!-- CREDITS:END -->