Image-Text-to-Text
GGUF
llama.cpp
rocm
amd
rocmfp4
rocmfpx
strix-halo
amd-strix-halo
gfx1151
ryzen-ai-max
ryzen-ai-max-395
radeon-8060s
Mixture of Experts
reasoning
multimodal
vision
nex
qwen3.5
quantized
conversational
Instructions to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF 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 kingjones777/Nex-N2.5-mini-ROCmFP4-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 kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
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/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
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/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
Use Docker
docker model run hf.co/kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
- LM Studio
- Jan
- vLLM
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kingjones777/Nex-N2.5-mini-ROCmFP4-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": "kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
- Ollama
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF with Ollama:
ollama run hf.co/kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
- Unsloth Desktop
- Pi
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
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": "kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF with Docker Model Runner:
docker model run hf.co/kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
- Lemonade
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
Run and chat with the model
lemonade run user.Nex-N2.5-mini-ROCmFP4-GGUF-Q4_0
List all available models
lemonade list
- Hermes Agent
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-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 kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
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/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0
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/Nex-N2.5-mini-ROCmFP4-GGUF:Q4_0" \ --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"
| #!/usr/bin/env python3 | |
| """Which server-side default makes Nex behave well for clients that pass no chat_template_kwargs (the gateway case)? | |
| Follow-up to nex_tools_tpl.py: with the `high` shim, a no-kwargs one-word request came back entirely in | |
| reasoning_content (the model never closed its forced-open think block). Two candidate defaults, greedy probes: | |
| C1 high shim + `--chat-template-kwargs {"enable_thinking": false}` (thinking off unless a client asks) | |
| C2 v2 shim (explicit enable_thinking=false wins; otherwise enable_thinking maps to high unless reasoning_effort is | |
| set) + `--chat-template-kwargs {"reasoning_effort": "medium"}` (upstream's adaptive default) | |
| Diagnostic only -> results/nex_seat_default_probe.json.""" | |
| import json, os, subprocess, sys, time | |
| from types import SimpleNamespace | |
| os.environ["AGNES_BIN"] = "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin" | |
| sys.path.insert(0, "/mnt/models/nex-n2.5-mini") | |
| import nex_harness as H # noqa: E402 | |
| W = H.W | |
| SRC = open(f"{W}/hf/chat_template.jinja", "rb").read() | |
| SHIM_V2 = ("{%- if enable_thinking is defined and not enable_thinking %}{%- set reasoning_effort = 'none' %}" | |
| "{%- elif reasoning_effort is not defined and enable_thinking is defined %}" | |
| "{%- set reasoning_effort = 'high' %}{%- endif %}\n") | |
| TPL_V2 = f"{W}/tpl/chat_template_enable_thinking_v2.jinja" | |
| open(TPL_V2, "wb").write(SHIM_V2.encode() + SRC) | |
| CONFIGS = [ | |
| ("C1", [f"--chat-template-file", f"{W}/tpl/chat_template_enable_thinking.jinja", | |
| "--chat-template-kwargs", json.dumps({"enable_thinking": False})]), | |
| ("C2", ["--chat-template-file", TPL_V2, "--chat-template-kwargs", json.dumps({"reasoning_effort": "medium"})]), | |
| ] | |
| PROMPTS = [("correct-decline", "What is 17 times 23? Answer directly."), | |
| ("single-word", "Reply with the single word: ready"), | |
| ("multi-arg", "What's the weather in Paris in celsius?")] | |
| KW = [("no-kwargs", None), ("enable_thinking=true", {"enable_thinking": True}), | |
| ("enable_thinking=false", {"enable_thinking": False})] | |
| _orig = subprocess.Popen | |
| report = {"shim_v2": SHIM_V2, "configs": {}} | |
| for name, extra in CONFIGS: | |
| def _popen(cmd, *a, _extra=extra, **k): | |
| if cmd and str(cmd[0]).endswith("llama-server"): | |
| cmd = list(cmd) + list(_extra) | |
| return _orig(cmd, *a, **k) | |
| subprocess.Popen = _popen | |
| a = SimpleNamespace(model=f"{W}/out/Nex-N2.5-mini-Q4_0_ROCMFP4_STRIX_LEAN.gguf", dev="ROCm0", ctx=16384, | |
| draft=None, mtp_infile=False, nmax=4, pmin=0.0, strict=False, | |
| serverlog=f"{W}/logs/probe_seat_default_{name}.log") | |
| try: | |
| s = H.Server(a, 18653) | |
| finally: | |
| subprocess.Popen = _orig | |
| rec = {"extra": extra, "results": {}} | |
| try: | |
| for kn, kw in KW: | |
| for pn, prompt in PROMPTS: | |
| body = {"messages": [{"role": "user", "content": prompt}], "tools": H.TOOLS, "tool_choice": "auto", | |
| "temperature": 0, "top_k": 1, "max_tokens": 2048} | |
| if kw is not None: | |
| body["chat_template_kwargs"] = kw | |
| try: | |
| m = H.post(18653, "/v1/chat/completions", body)["choices"][0]["message"] | |
| c = m.get("content") or "" | |
| r = {"content": c[:120], "reasoning_len": len(m.get("reasoning_content") or ""), | |
| "tool_calls": [(t.get("function") or {}).get("name") for t in m.get("tool_calls") or []], | |
| "leaks": [x for x in H.LEAK if x in c]} | |
| except Exception as e: # noqa: BLE001 | |
| r = {"error": repr(e)[:300]} | |
| rec["results"][f"{kn}|{pn}"] = r | |
| print(name, kn, pn, json.dumps(r)[:220], flush=True) | |
| finally: | |
| s.stop() | |
| report["configs"][name] = rec | |
| time.sleep(3) | |
| json.dump(report, open(f"{W}/results/nex_seat_default_probe.json", "w"), indent=1) | |
| print("NEX_SEAT_DEFAULT_PROBE_DONE") | |