Instructions to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-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 bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-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 bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-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": "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-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/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Ollama
How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF with Ollama:
```
ollama run hf.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_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": "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M
```

Lemonade

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ThinkingCap-Qwen3.8-Flash-Next-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-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 bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_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 bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_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 "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_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"

Adamiros commited on 1 day ago

Commit

b9474a5

verified ·

1 Parent(s): 2a053f7

Card: say why the MTP example uses --no-kv-unified

Browse files

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README.md +3 -3

README.md CHANGED Viewed

@@ -77,14 +77,14 @@ Checked with llama.cpp at commit `5f5acfe` (2026-10-22): `Q4_K_M` with the visio
 These GGUFs carry the model's MTP (multi-token-prediction) head, so llama.cpp can run self-speculative decoding — no separate draft model needed. Add `--spec-type draft-mtp` when serving:
 ```bash
-# -hf also loads the vision projector; -np 4 --no-kv-unified keeps parallel image requests accurate (see Vision below)
 llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M -np 4 --no-kv-unified --spec-type draft-mtp --spec-draft-n-max 4
 ```
-For text only, add `--no-mmproj` to skip the projector and drop `-np 4 --no-kv-unified`.
 Requires a llama.cpp build with MTP support for this architecture; runtimes that predate it may refuse to load the file with a `missing tensor` error — update the runtime. In that check on the H200, with 3 draft tokens (`--spec-draft-n-max 3`), MTP accepted on average 3.75 tokens per decoding step on the math request and 2.72 on the image request.
 ## Vision (image input)
 ThinkingCap is a **vision-language model**. Image input needs the multimodal projector

 These GGUFs carry the model's MTP (multi-token-prediction) head, so llama.cpp can run self-speculative decoding — no separate draft model needed. Add `--spec-type draft-mtp` when serving:
 ```bash
+# -hf also loads the vision projector
 llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF:Q4_K_M -np 4 --no-kv-unified --spec-type draft-mtp --spec-draft-n-max 4
 ```
 Requires a llama.cpp build with MTP support for this architecture; runtimes that predate it may refuse to load the file with a `missing tensor` error — update the runtime. In that check on the H200, with 3 draft tokens (`--spec-draft-n-max 3`), MTP accepted on average 3.75 tokens per decoding step on the math request and 2.72 on the image request.
+`-np 4 --no-kv-unified` gives each of the 4 parallel requests its own KV cache. llama-server's slots share one cache by default, and with this model that costs accuracy when several image requests are served at once: on 100 RealWorldQA questions the default 4 shared slots scored 71.0% against 86.0% for a single slot (see Vision below). For text only, add `--no-mmproj` to skip the projector and drop `-np 4 --no-kv-unified`.
 ## Vision (image input)
 ThinkingCap is a **vision-language model**. Image input needs the multimodal projector