Instructions to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF",
	filename="Kimi-Linear-48B-A3B-Instruct-APEX-balanced.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

Use Docker

docker model run hf.co/Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

LM Studio
Jan

vLLM

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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": "Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

Ollama
How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with Ollama:
```
ollama run hf.co/Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF
```

Unsloth Studio

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF to start chatting

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

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": "Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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 "Myric/Kimi-Linear-48B-A3B-Instruct-APEX-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"

Docker Model Runner
How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF
```

Lemonade

How to use Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Myric/Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

Run and chat with the model

lemonade run user.Kimi-Linear-48B-A3B-Instruct-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Kimi-Linear-48B-A3B-Instruct-APEX-GGUF

File size: 1,783 Bytes

cbbf98d

# Reproducing these APEX quants

No-imatrix, CPU-only path. All MIT (see NOTICE).

## Pinned
- **llama.cpp** — a build supporting `kimi_linear` + `kimi-k2` pre-tokenizer +
  `--tensor-type-file` (e.g. commit `bbf4a8a`/b8833 or newer).
- **apex-quant** — commit `a445a12` (https://github.com/localai-org/apex-quant),
  for `generate_config.sh` (bundled here).

## Baseline
```bash
hf download bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF \
  --include "moonshotai_Kimi-Linear-48B-A3B-Instruct-bf16/*" --local-dir .
```
(A correctly-converted bf16 GGUF: kimi-k2 tokenizer with BPE merges present.)

## Config (imatrix-free "balanced")
The final configs are included (`configs/kimi_balanced.txt`, `configs/kimi_handroll.txt`).
To regenerate:
```bash
# 43 layers, layer 0 dense
bash generate_config.sh --profile balanced --layers 43 --dense-layers 1 -o kimi_bal.base.txt
# add the MLA + KDA(ssm) tensors the stock generator misses
python patch_kimi_config.py kimi_bal.base.txt configs/kimi_balanced.txt
# hand-roll variant: KDA recurrence pinned to Q8_0 (found to make no PPL difference)
python patch_kimi_config.py kimi_bal.base.txt configs/kimi_handroll.txt --ssm-type Q8_0
```

## Quantize (base type Q6_K; NO imatrix)
```bash
SHARD=moonshotai_Kimi-Linear-48B-A3B-Instruct-bf16/moonshotai_Kimi-Linear-48B-A3B-Instruct-bf16-00001-of-00003.gguf
llama-quantize --tensor-type-file configs/kimi_balanced.txt \
  "$SHARD" Kimi-Linear-48B-A3B-Instruct-APEX-balanced.gguf Q6_K
```

## Evaluate
```bash
# NOTE: PPL prints to STDERR — capture 2>&1.
llama-perplexity -m Kimi-Linear-48B-A3B-Instruct-APEX-balanced.gguf \
  -f wiki.test.raw -ngl 999 --chunks 200 2>&1 | grep -oP 'Final estimate: PPL = \K[0-9.]+'
```
Expected: ~7.38 (wikitext-2, 200×512). bf16 reference pending.