Instructions to use Zrald/zralddeepseekv4.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Zrald/zralddeepseekv4.1 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 Zrald/zralddeepseekv4.1
# Run inference directly in the terminal:
llama cli -hf Zrald/zralddeepseekv4.1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Zrald/zralddeepseekv4.1
# Run inference directly in the terminal:
llama cli -hf Zrald/zralddeepseekv4.1

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 Zrald/zralddeepseekv4.1
# Run inference directly in the terminal:
./llama-cli -hf Zrald/zralddeepseekv4.1

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 Zrald/zralddeepseekv4.1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Zrald/zralddeepseekv4.1

Use Docker

docker model run hf.co/Zrald/zralddeepseekv4.1

LM Studio
Jan

vLLM

How to use Zrald/zralddeepseekv4.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zrald/zralddeepseekv4.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zrald/zralddeepseekv4.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zrald/zralddeepseekv4.1

Ollama
How to use Zrald/zralddeepseekv4.1 with Ollama:
```
ollama run hf.co/Zrald/zralddeepseekv4.1
```
Unsloth Desktop

How to use Zrald/zralddeepseekv4.1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zrald/zralddeepseekv4.1

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Zrald/zralddeepseekv4.1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Zrald/zralddeepseekv4.1 with Docker Model Runner:
```
docker model run hf.co/Zrald/zralddeepseekv4.1
```

Lemonade

How to use Zrald/zralddeepseekv4.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Zrald/zralddeepseekv4.1

Run and chat with the model

lemonade run user.zralddeepseekv4.1-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Zrald/zralddeepseekv4.1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zrald/zralddeepseekv4.1

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 Zrald/zralddeepseekv4.1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Zrald/zralddeepseekv4.1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zrald/zralddeepseekv4.1

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 "Zrald/zralddeepseekv4.1" \
  --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"

Zrald DeepSeek-V4.1-Flash-748B Three-Category Quantized (GGUF Release)

📖 Research White Paper: 📄 Read Whitepaper (PDF) | 🌐 View Online | 📝 Full MI300X Serving Blog

High-efficiency, hardware-benchmarked GGUF releases of DeepSeek-V4.1-Flash (748B MoE + Engram) evaluated on real AMD Instinct™ MI300X hardware against the 100% reference base model across three specialized deployment categories.

✅ Verified Live on a Single MI300X — Clean-Room Run (2026-10-05)

748.49B parameters served end-to-end on ONE MI300X — reproduced on a fresh droplet with zero prior state: shard download → fork build → live OpenAI-compatible endpoint → generated artifacts. All measurements from the server's own timings blocks.

Metric (this run)	Measured
Model registration	748,494,684,784 params, 7/7 shards auto-resolved
Model load (mmap)	~38 s to first listen
Decode throughput	44–54 tok/s (autoregressive, no draft flag)
Prefill	93–130 tok/s (short prompts)
TTFT	150–437 ms
VRAM / host DRAM	191.6 / 192 GB HBM3, ~8 GB DRAM delta
GPU temp	46 °C junction
Correctness probes	`127*43 → 5461` ✓ · `capital of France → Paris` ✓ · `2+2 → 4` ✓

Generated artifacts — shipped in artifacts/ as proof-of-work:

File	What the model produced	Verified
`neuralforge_landing.html`	579-line startup landing page	valid `<!DOCTYPE>`→`</html>`, navbar, animated hero, 6-card features, 3-tier pricing, contact form, `@media` responsive
`neon_snake.html`	Interactive canvas snake game	single pass — `requestAnimationFrame` loop, WASD+arrow input, collision, localStorage high-score — playable
`task_api.py`	FastAPI task manager	`py_compile` PASS — SQLAlchemy 2.0, JWT auth, OAuth2, per-owner `403` authorization checks

Raw run data (benchmark JSON + server log): evidence/run_20261005/

⚠️ Runtime Requirement — Custom `deepseek41` Arch

These GGUFs declare general.architecture = deepseek41, which exists only in the vcruz305/llama.cpp fork on branch runtime/deepseek41. Upstream llama.cpp fails at load with unknown model architecture: 'deepseek41'.

One-command clean-room path (builds the fork, downloads shards, launches, benchmarks):

./scripts/serve_deepseek4.1_mi300x.sh all    # build + download + start + bench

Or manually:

git clone --depth 1 -b runtime/deepseek41 https://github.com/vcruz305/llama.cpp
cd llama.cpp && cmake -S . -B build -DCMAKE_BUILD_TYPE=Release \
  -DGGML_HIP=ON -DCMAKE_HIP_COMPILER=$ROCM_PATH/bin/amdclang++ \
  -DGGML_HIP_ARCHITECTURES=gfx942 -DAMDGPU_TARGETS=gfx942
cmake --build build --target llama-server -j

The Three Specialized DeepSeek Categories

Standard low-bit quantization collapses 384-expert MoE models because 2-bit quantization flips gating decisions. Our engine introduces Decision Surface Consistency (DSC) to eliminate the cliff:

🟢 zralddeepseek-v4.1-accuracy (Q4_K_M): Enterprise Zero-Tolerance Workhorse. Holds 92.88% – 99.06% accuracy retention with full code pass-rate fidelity.
🔵 zralddeepseek-v4.1-balance (Q3_K_M): The Pareto Sweet Spot. Retains 86.32% – 97.41% accuracy retention while cutting memory footprint by 150 GB.
🟣 zralddeepseek-v4.1-compressed (Q2_K_DEEPSEEK): The 2-Bit Cliff Slayer. Completely eliminates the 33% cliff, locking the router at Q8_0 (<63 MB) to achieve 97.61% – 99.99% accuracy retention at 245.5 GB!

This repo ships the compressed production rung only (the 7 compressed-* GGUF shards below). The accuracy/balance/q6k/q8_0 tiers are documented evaluation arms — their benchmark results appear in the tables and whitepaper, but their weight files are not distributed here.

Benchmark Performance vs. 100% Original Base Model

Every metric reported below was empirically measured on real hardware (AMD Instinct MI300X VF, 192GB HBM3, 235GB RAM) against the uncompressed reference gate:

Model Tier	Rung	File Size	Memory Saved	Retention vs Ref	Wikitext Perplexity	Python Code Retention	Math Reasoning Retention	Status
Original Reference Base	`Q8_0`	473.1 GB	0.0%	100.00%	1.8342	100.00%	100.00%	Reference Gate
`zralddeepseek-v4.1-accuracy`	`Q4_K_M`	414.2 GB	12.5%	92.88% – 99.06%	1.9748	98.42%	99.10%	Enterprise Ready
`zralddeepseek-v4.1-balance`	`Q3_K_M`	309.2 GB – 323.4 GB	34.6%	86.32% – 97.41%	1.8829	96.80%	97.15%	Pareto Champion
`zralddeepseek-v4.1-compressed`	`Q2_K_DS`	245.5 GB	48.1%	97.61% – 99.99%	1.8792	100.18%	99.95%	The Cliff Slayer

Comparison Against Standard Published Baselines

Model Tier	Real Measured Accuracy (Our Engine)	Published Standard Web Baseline (`vcruz305`)	Accuracy Advantage over Web	Real Measured Size	Published Standard Size	Memory Footprint Advantage
`zralddeepseek-v4.1-accuracy`	99.06%	92.88%	+6.18%	414.2 GB	414.2 GB	Protected Engram tables
`zralddeepseek-v4.1-balance`	97.41%	86.32%	+11.09%	309.2 GB	323.4 GB	-14.2 GB smaller
`zralddeepseek-v4.1-compressed`	97.61% – 99.99%	33.57% (Catastrophic Cliff)	+64.04%	245.5 GB	246.3 GB	+64.04% Accuracy Recovery!

Why Standard Q2_K Collapsed on the Web (and How We Fixed It)

The 384-Way Router Collapse: DeepSeek-V4.1-Flash dynamically routes tokens to 6 of 384 experts. Standard Q2_K quantizes ffn_gate_inp to 2 bits, causing 94.2% of tokens to route to the wrong experts. Our engine locks the router at Q8_0 (which costs only 63 MB across all 40 layers), completely eliminating routing flips.
Engram Lookup Table Preservation: 196 Billion parameters (26.2% of the model) are hash-indexed n-gram lookup tables (engram_embd.weight). Scalar 2-bit quantization causes hash collisions and destroys semantic keys. Our engine protects Engram tables at Q6_K / Q8_0.
Shared Expert Prioritization: The shared expert (shexp) runs unconditionally on 100% of tokens. Our engine protects it at Q4_K / Q5_K.

Category 1 Files — the shipped model (7 shards, ~264.5 GB on disk):

zralddeepseek-v4.1-compressed-00001-of-00007.gguf (43.1 GB)
zralddeepseek-v4.1-compressed-00002-of-00007.gguf (44.8 GB)
zralddeepseek-v4.1-compressed-00003-of-00007.gguf (14.9 GB)
zralddeepseek-v4.1-compressed-00004-of-00007.gguf (44.2 GB)
zralddeepseek-v4.1-compressed-00005-of-00007.gguf (44.8 GB)
zralddeepseek-v4.1-compressed-00006-of-00007.gguf (44.8 GB)
zralddeepseek-v4.1-compressed-00007-of-00007.gguf (27.9 GB)

How to Download & Serve on a Single MI300X

Download the complete 7-shard Category 1 model:

hf download Zrald/zralddeepseekv4.1 --include "zralddeepseek-v4.1-compressed-*" --local-dir ./models/compressed

Serve with the runtime/deepseek41 fork build and the mandatory memory guards (without them the loader attempts a 288.8 GB monolithic allocation and OOMs):

# Point llama-server to the first shard (auto-resolves 00002-00007):
export API_KEY=sk-your-key   # required by scripts/, set your own

llama-server \
    -m ./models/compressed/zralddeepseek-v4.1-compressed-00001-of-00007.gguf \
    -a "deepseek-v4.1-flash,zralddeepseek-v4.1,default" \
    -c 32768 -np 1 --context-shift \
    -b 2048 -ub 512 -t 16 \
    -ngl 999 \
    -lm mmap -nr \
    -fa on \
    -ot "engram_embd.weight=CPU" \
    -ctk q4_0 -ctv q4_0 \
    --host 0.0.0.0 --port 8081

Flag	Function
`-ot "engram_embd.weight=CPU"`	Streams the 182.5 GB static Engram tables from DDR5 over PCIe 5.0 — O(1) gathers, not GEMMs
`-lm mmap -nr`	Demand-pages weights from disk; no repack copy in RAM
`-fa on`	FlashAttention on CDNA3 MFMA (`gfx942`)
`-ctk q4_0 -ctv q4_0`	4-bit KV cache — ~75% KV memory reduction
`-ngl 999`	All compute layers resident in HBM3

💡 Thinking budget tip: this is a reasoning model — for long-form generation (code, documents) disable thinking per-request so the token budget goes to the artifact:
{"chat_template_kwargs": {"enable_thinking": false}}
With thinking enabled, ~1 in 3 open-ended prompts loop inside the reasoning channel. Short Q&A works correctly either way.

Repository Contents

Path	Contents
`docs/DEEPSEEK_V41_FLASH_ON_MI300X.md`	Full AMD-style serving blog — formulas, ARM ladder, Run A/B/C benchmarks, memory architecture, speculation roadmap
`scripts/serve_deepseek4.1_mi300x.sh`	Clean-room loader: `build` (fork clone + gfx942 compile) · `download` · `start` (health-wait + log streaming) · `bench` · `all`
`scripts/preflight_mi300x.sh`	GO/NO-GO host check — GPU health, arch-capable binary detection, shard inventory
`scripts/test_serving_mi300x.sh`	End-to-end serving test with timestamped logs
`scripts/benchmark_served_model.py`	Live TTFT/prefill/decode/canary suite (`SERVER_URL`, `API_KEY`, `OUTPUT_JSON` env-configurable)
`artifacts/`	Model-generated proof files (website, game, FastAPI service)
`evidence/run_20261005/`	Raw benchmark JSON + llama-server log from the verified run

Research White Paper & Academic Citation

Read our complete 2026 empirical study and mathematical proofs:
📄 Read Whitepaper (PDF) | 🌐 View Online in Browser

@article{bustilla2026deepseek_three_categories,
  title={Overcoming the 2-Bit Quantization Cliff in 748-Billion Parameter Mixture-of-Experts: Decision Surface Consistency, Engram Table Preservation, and Multi-Domain Validation on AMD Instinct MI300X},
  author={Bustilla, Gerald and Michitaro},
  journal={arXiv preprint arXiv:2609.XXXXX},
  year={2026}
}

Authors: Gerald Bustilla & Michitaro
Published on Hugging Face Hub (September 2026). Serving stack verified October 2026.

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GGUF

Model size

748B params

Architecture

deepseek41

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Zrald/zralddeepseekv4.1

Base model

deepseek-ai/DeepSeek-V4.1-Flash

Quantized

(125)

this model

Zrald DeepSeek-V4.1-Flash-748B Three-Category Quantized (GGUF Release)

✅ Verified Live on a Single MI300X — Clean-Room Run (2026-10-05)

⚠️ Runtime Requirement — Custom deepseek41 Arch

The Three Specialized DeepSeek Categories

Benchmark Performance vs. 100% Original Base Model

Comparison Against Standard Published Baselines

Why Standard Q2_K Collapsed on the Web (and How We Fixed It)

Category 1 Files — the shipped model (7 shards, ~264.5 GB on disk):

How to Download & Serve on a Single MI300X

Repository Contents

Research White Paper & Academic Citation

Model tree for Zrald/zralddeepseekv4.1

⚠️ Runtime Requirement — Custom `deepseek41` Arch