Instructions to use sakamakismile/Nex-N2.5-Max-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sakamakismile/Nex-N2.5-Max-GGUF 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 sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

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 sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

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 sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

Use Docker

docker model run hf.co/sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

LM Studio
Jan

vLLM

How to use sakamakismile/Nex-N2.5-Max-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sakamakismile/Nex-N2.5-Max-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sakamakismile/Nex-N2.5-Max-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

Ollama
How to use sakamakismile/Nex-N2.5-Max-GGUF with Ollama:
```
ollama run hf.co/sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M
```
Unsloth Desktop

How to use sakamakismile/Nex-N2.5-Max-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

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": "sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sakamakismile/Nex-N2.5-Max-GGUF with Docker Model Runner:
```
docker model run hf.co/sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M
```

Lemonade

How to use sakamakismile/Nex-N2.5-Max-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Nex-N2.5-Max-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use sakamakismile/Nex-N2.5-Max-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

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 sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sakamakismile/Nex-N2.5-Max-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M

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 "sakamakismile/Nex-N2.5-Max-GGUF:Q3_K_M" \
  --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"

Nex-N2.5-Max-GGUF

File size: 3,169 Bytes

1a09ed0

diff --git a/conversion/deepseek.py b/conversion/deepseek.py
index 817eb76..a49f27a 100644
--- a/conversion/deepseek.py
+++ b/conversion/deepseek.py
@@ -694,7 +694,11 @@ class DeepseekV4Model(TextModel):
 
         def dequant_fp8_weight(weight: Tensor, scale: Tensor) -> Tensor:
             out_features, in_features = weight.shape
-            scale_f = self._e8m0_to_float(scale)
+            # Lna-Lab 2026-09-09: Nex-N2.5-Max stores block scales as F32 values (ue8m0 semantics), not e8m0 bytes
+            if scale.dtype in (torch.float32, torch.float16, torch.bfloat16):
+                scale_f = scale.float()
+            else:
+                scale_f = self._e8m0_to_float(scale)
             scale_f = scale_f.repeat_interleave(128, 0)[:out_features]
             scale_f = scale_f.repeat_interleave(128, 1)[:, :in_features]
             return weight.float() * scale_f
@@ -780,6 +784,20 @@ class DeepseekV4Model(TextModel):
         for bid in range(self.block_count):
             if self.mtp_only and bid < main_layers:
                 continue
+            if f"layers.{bid}.ffn.experts.0.w1.weight" in self._dsv4_fp8_dequantized:
+                # Lna-Lab 2026-09-09: FP8 routed experts (Nex-N2.5-Max) -> stack per projection, no MXFP4 repack
+                n_experts = self.hparams["n_routed_experts"]
+                for proj in ("w1", "w2", "w3"):
+                    parts = []
+                    for eid in range(n_experts):
+                        wname = f"layers.{bid}.ffn.experts.{eid}.{proj}.weight"
+                        parts.append(self.model_tensors[wname]().to(torch.bfloat16))  # lazy: evaluated at write time
+                        consumed.append(wname)
+                    stacked_name = f"layers.{bid}.ffn.experts.{proj}.weight"
+                    self._dsv4_fp8_dequantized.add(stacked_name)
+                    logger.info(f"{stacked_name}: stacked {n_experts} FP8-dequantized experts")
+                    yield (stacked_name, torch.stack(parts, dim=0))
+                continue
             consumed.extend(self._write_mxfp4_expert_tensor(bid, "w1", gguf.MODEL_TENSOR.FFN_GATE_EXP))
             consumed.extend(self._write_mxfp4_expert_tensor(bid, "w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP))
             consumed.extend(self._write_mxfp4_expert_tensor(bid, "w3", gguf.MODEL_TENSOR.FFN_UP_EXP))
@@ -857,6 +875,9 @@ class DeepseekV4Model(TextModel):
             "ffn.gate.bias": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, ".bias"),
             "ffn.gate.bias_vl": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B_VL, ".bias"),
             "ffn.gate.tid2eid": (gguf.MODEL_TENSOR.FFN_GATE_TID2EID, ".weight"),
+            "ffn.experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_EXP, ".weight"),
+            "ffn.experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_EXP, ".weight"),
+            "ffn.experts.w3.weight": (gguf.MODEL_TENSOR.FFN_UP_EXP, ".weight"),
             "ffn.shared_experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
             "ffn.shared_experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
             "ffn.shared_experts.w3.weight": (gguf.MODEL_TENSOR.FFN_UP_SHEXP, ".weight"),