Instructions to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-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 Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-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 Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS
# Run inference directly in the terminal:
llama cli -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS
# Run inference directly in the terminal:
llama cli -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

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 Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS
# Run inference directly in the terminal:
./llama-cli -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

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 Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

Use Docker

docker model run hf.co/Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

LM Studio
Jan

vLLM

How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-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": "Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

Ollama
How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with Ollama:
```
ollama run hf.co/Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS
```

Unsloth Studio

How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF to start chatting

How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

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 "Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS" \
  --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"

Docker Model Runner
How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with Docker Model Runner:
```
docker model run hf.co/Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS
```

Lemonade

How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

Run and chat with the model

lemonade run user.BTL-4-IQ2_XXS-DSPARK-GGUF-IQ2_XXS

List all available models

lemonade list

Hermes Agent

How to use Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-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 Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

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 Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF:IQ2_XXS

Run Hermes

hermes

Atomic Chat

BTL-4 IQ2_XXS + Qwen3.6 DSpark GGUF

A self-contained, tested pairing of badtheorylabs/BTL-4-Compact and the Qwen3.6 DSpark GGUF draft converted by williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF.

BTL-4 is Qwen3.6/Ornith-derived and proved compatible with this draft in the experimental llama.cpp DSpark verifier. The weights are unchanged from the upstream releases. This repository adds a reproducible launch profile and RTX 5090 measurements at a 200,704-token configured context window.

DSpark support is experimental. These results use llama.cpp PR #26275, commit f806441edb5006fdacb07df42445f337476dd169.

Files

File	Purpose	Size
`BTL-4-IQ2_XXS.gguf`	verifier/target model	9.97 GB
`Qwen3.6-35B-A3B-DSPARK.gguf`	DSpark draft model; not standalone	1.04 GB
`launch-dspark.sh`	tested 200K launch profile	—
`benchmark-results.json`	machine-readable measurements	—

RTX 5090 generation benchmark

Measured locally on 2026-08-07. Each result is an end-to-end wall-clock OpenAI Chat Completions request generating 512 tokens. Values are three independent runs after loading the full 200,704-token context configuration.

Configuration	Runs (tok/s)	Mean	Best	VRAM	Relative to baseline
No draft	260.51, 267.85, 271.10	266.49 tok/s	271.10	12,586 MiB	1.000x
DSpark, max draft 3	275.16, 307.40, 312.49	298.35 tok/s	312.49	17,264 MiB	1.120x

The DSpark run accepted 326 of 553 drafted tokens (58.95%) with mean accepted length 2.76. --spec-draft-n-max 3 is recommended; longer draft settings were already slower on the Qwen3.6 Aggressive target tested on the same machine.

Test machine

Component	Value
GPU	NVIDIA GeForce RTX 5090, 32,607 MiB
Driver / power limit	595.84 / 575 W
OS / kernel	Ubuntu 26.04 LTS / Linux 7.0.0-28-generic x86_64
Target quantization	IQ2_XXS experts / Q4_K_M mixture, 9.97 GB
Draft	BF16 GGUF, 1.04 GB
Configured context	200,704 tokens
KV cache	Q8_0 K and V for both target and draft
Batch / microbatch	2,048 / 512
Parallel slots	1
Flash attention	enabled

These are single-stream decode measurements, not prompt-processing throughput or multi-user aggregate throughput. The first request can include warm-up overhead, which is intentionally retained in the mean.

Quality benchmarks and quantization caveat

The upstream full-precision badtheorylabs/BTL-4 model card reports the following official-harness results:

Benchmark	Upstream BTL-4 BF16	Attribution
LiveCodeBench v6	66.1% pass@1	Bad Theory Labs; 442 problems, 2024-08 through 2025-05
SWE-bench Verified	78.4%	Bad Theory Labs; official harness
BFCL v4 AST	73.5%	Bad Theory Labs; 1,240 cases

Those values are upstream-reported BF16 scores, not measurements of this IQ2_XXS file. The Compact card reports 94.1% behavioral retention (111/118 on its quantization replay gate), but LiveCodeBench and SWE-bench Verified were not rerun for the compact quant. Do not present 66.1% or 78.4% as measured compact scores.

DSpark is target-verifying speculative decoding: accepted draft tokens are verified by BTL-4, so it accelerates this quantized target without replacing the target's token decisions.

Run

Build the experimental branch with CUDA enabled:

git clone --branch dspark-speculators https://github.com/wjinxu/llama.cpp.git
cmake -S llama.cpp -B llama.cpp/build \
  -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CUDA_ARCHITECTURES=120a
cmake --build llama.cpp/build --config Release -j --target llama-server

Then, from this repository directory:

LLAMA_SERVER=/path/to/llama.cpp/build/bin/llama-server ./launch-dspark.sh

OpenAI-compatible API:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "btl-4-compact-dspark",
    "messages": [{"role": "user", "content": "Refactor this function."}],
    "temperature": 0,
    "max_tokens": 512
  }'

Attribution and limitations

Compact target: badtheorylabs/BTL-4-Compact
Full-precision target and reported quality benchmarks: badtheorylabs/BTL-4
Draft conversion: williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF
Draft lineage: Koopah/Qwen3.6-35B-A3B-NVFP4-DSPARK
Runtime: llama.cpp PR #26275

The upstream compact build is text-only and has its MTP layer disabled. The external DSpark draft is separate from MTP. See THIRD_PARTY_NOTICES.md and LICENSE.

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GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

View +1 variant

Model tree for Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF

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badtheorylabs/BTL-4

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