Instructions to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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 Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF
# Run inference directly in the terminal:
llama cli -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF
# Run inference directly in the terminal:
llama cli -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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 Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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 Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

Use Docker

docker model run hf.co/Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

LM Studio
Jan

vLLM

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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": "Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

Ollama
How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with Ollama:
```
ollama run hf.co/Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF
```
Unsloth Desktop

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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": "Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with Docker Model Runner:
```
docker model run hf.co/Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF
```

Lemonade

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

Run and chat with the model

lemonade run user.TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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 Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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 Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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 "Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-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"

Model Card for Model ID

Skyfall is a heavy hitter for mainline consumer GPUs. I used to run it at IQ4_XS on 16 GB with partial offload, which was way too slow.
IQ3_M was tolerable, but restrictive of KVO. IQ3_XXS was permissive of KVO, but too dumbed down.
This is basically a ik-llama.cpp answer to "how do I fit this model into 16 gb with KVO without exacerbating brain damage"
The goals: at least not dumber than IQ3_M, at least 32k context in VRAM, at least at Q5_0 K and Q4_1 V cache, with system overhead accounted for.
The result: a mainly IQ3_KT quantization of TheDrummer/Skyfall-31B-v4.2

Quant Details

iq3_s: token_embd (1)
iq3_kt: attn_k, attn_q, ffn_up, ffn_gate, ffn_down (270)
iq4_k: attn_output, attn_v, output (109)
imatrix by bartowski

quantized with ik_llama.cpp build: 9d07d868

incompatible with mainline llama.cpp
only tested with ik_llama.cpp (though I expect croco.cpp to work as well)

Comparison:

IQ3_M by bartowski:

winogrande: 74.9013 ± 1.2186 (949/1267)
hellaswag (n=400, s=42): 82.25%

with K q5_0, V q4_1, b=512, amb=64, ngl=999, 5 runs:

pp512: 899.37 ± 36.62
pp32768: 658.53 ± 2.11
tg128: 25.09 0± 0.39
tg512: 22.00 ± 0.02
size: 14314095296 bytes

this IQ3_KT:

winogrande: 75.5328 ± 1.2082 (957/1267) (+0.84%)
hellaswag (n=400, s=42): 82.75% (+0.61%)

with K q5_0, V q4_1, b=512, amb=64, ngl=999, 5 runs:

pp512: 975.41 ± 32.69 (+8.45%)
pp32768: 741.10 ± 7.88 (+12.54%)
tg128: 23.16 ± 0.08 (-7.69%)
tg512: 22.53 ± 0.15 (+2.5%)
size: 12658394016 bytes (11.57% smaller) (-1578.99 MB)

In conclusion

-Is it smarter?
Not in a statistically significant way, but it's not dumber.

-Is it slower?
No. It's actually faster in attn, which is not surprising, given less quality on attn layers in comparison to IQ3_M by bartowski.

-Is it smaller?
Hell yeah. -1579 MB puts it in IQ3_XXS territory by size

-Is it optimal?
No. I have a suspicion lowered attn quants will hurt it over long ctx (to be tested). Could try squeezing ffn_up in favor of k- and q- attn.
The primary candidate directly touching residuals is ffn_down which I can't quant up meaningfully (IQ3_KT is IQ_3M-ish in quality, going above 4bit bloats the gguf hard, failing the ctx goal.
One quantization takes me about 2.5 hrs on my CPU, so I can't freely test every hypothesis quickly.

-Is it worth it?
No idea. I made it for personal use to squeeze some extra context in without losing brainpower. I already had to resort to 3 bits. WYSIWYG.

-Possible 12G VRAM full fit with KVO?
Unlikely. A mixed IQ2_KT ffn / IQ3_KT attn quant was around 8.8 GB, but it scored significantly worse on hellaswag and winogrande. I didn't go for testing it in RP, since it was overkill compression for my purpose.

-Why Q5_0/Q4_1?
Reasons

Cheers

MistralAI - the beloved base model(s).
ikawrakow and contributors of ik_llama.cpp - I probably misused your wonderful creation.
TheDrummer - you cooked a legendary one.
bartowski - for the imatrix + the myriad of quants we all benefit from.

Downloads last month: 60

GGUF

Model size

31B params

Architecture

llama

Hardware compatibility

Model tree for Koshkasa/TheDrummer_Skyfall-31B-v4.2_IQ3_KT-GGUF

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

Finetuned

mistralai/Mistral-Small-3.2-24B-Instruct-2506

Finetuned

mistralai/Magistral-Small-2509

Finetuned

TheDrummer/Skyfall-31B-v4.2

Quantized

(15)

this model