Instructions to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-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 ji-farthing/Qwen3.8-Flash-Next-ik-llama-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 ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF
# Run inference directly in the terminal:
llama cli -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF
# Run inference directly in the terminal:
llama cli -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

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 ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

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 ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

Use Docker

docker model run hf.co/ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

Llama new
LM Studio
Jan

vLLM

How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ji-farthing/Qwen3.8-Flash-Next-ik-llama-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": "ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

Ollama
How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF
```
Unsloth Desktop

How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

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": "ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF
```

Lemonade

How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-ik-llama-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-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 ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

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 ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF

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 "ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF" \
  --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"

ji-farthing commited on Sep 4

Commit

ceef7fe

verified ·

1 Parent(s): a0da4e8

Update README.md

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README.md +8 -8

README.md CHANGED Viewed

@@ -46,19 +46,19 @@ The two targets differ only in the routed experts: `ffn_gate_exps` and `ffn_up_e
 The head carries its own hyper-connection mixer, so the loader binds that rather than falling back to the trunk's. Its importance matrix was collected paired against the IQ3_KT target, on the same corpus as the targets, so the head is calibrated for the activations the quantized target actually produces.
 ```bash
-llama-server \
-  -m Qwen3.8-Flash-Next-ik_llama-IQ3_KT-00001-of-00002.gguf \
-  --model-draft Qwen3.8-Flash-Next-MTP-ik_llama-IQ4_KT.gguf \
-  --spec-type mtp:n_max=1,p_min=0.0 \
-  --defer-ple -ngl 4 -ot '(attn_|ssm_|hc_|indexer|ple_|shexp|ffn_gate_inp)=CUDA0' \
-  -c 16384 -b 2048 -ub 1024 -fa on -ctk q8_0 -ctv q8_0 -ictk q8_0 --jinja
 ```
-Validated with RTX 4070 with 12 GB VRAM and 64 GB system RAM: dense tensors on the GPU, experts on host, PLE table left on disk.
 ## Runtime
-These files need [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp). The KT trellis types exist only there. The targets load on current `main`. The draft head needs a build that includes qwen4exp MTP support (PR #2369 at the time of writing) and merged `--defer-ple` (PR #2389).
 `--defer-ple` matters on any machine with less RAM than the file. The PLE table is 26.8 GiB and is read a few rows per token, so it can stay on disk. Without the flag the loader pulls it resident. The routed experts are read every token and do need to fit in RAM or VRAM: **50.4 GiB for IQ3_KT, 58.6 GiB for IQ4_KT**.

 The head carries its own hyper-connection mixer, so the loader binds that rather than falling back to the trunk's. Its importance matrix was collected paired against the IQ3_KT target, on the same corpus as the targets, so the head is calibrated for the activations the quantized target actually produces.
 ```bash
+GGML_CUDA_NO_PINNED=1 llama-server \
+   -m Qwen3.8-Flash-Next-ik_llama-IQ3_KT-00001-of-00002.gguf \
+   --model-draft Qwen3.8-Flash-Next-MTP-ik_llama-IQ4_KT.gguf \
+   --spec-type mtp:n_max=1,p_min=0.0 \
+   --defer-ple -ngl 49 -ot 'ffn_.*_exps=CPU' \
+   -c 131072 -b 2048 -ub 2048 -fa on -ctk q8_0 -ctv q8_0 -ictk q8_0 --jinja
 ```
+Validated with RTX 4070 with 12 GB VRAM and 64 GB system RAM: every layer on the GPU except the routed experts, which stay on host, and the PLE table left on disk. This serves 128K context in 6.7 GiB of VRAM at about 20 tokens per second.
 ## Runtime
+These files need [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp). The KT trellis types exist only there. The targets load on current `main`. The draft head needs a build that includes qwen4exp MTP support (PR #2369).
 `--defer-ple` matters on any machine with less RAM than the file. The PLE table is 26.8 GiB and is read a few rows per token, so it can stay on disk. Without the flag the loader pulls it resident. The routed experts are read every token and do need to fit in RAM or VRAM: **50.4 GiB for IQ3_KT, 58.6 GiB for IQ4_KT**.