Instructions to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF", dtype="auto")

llama-cpp-python

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF",
	filename="Kimi-Linear-48B-A3B-Instruct-abliterated-BPW3.00.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 ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF
# Run inference directly in the terminal:
llama-cli -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF
# Run inference directly in the terminal:
llama-cli -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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 ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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 ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

Use Docker

docker model run hf.co/ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

LM Studio
Jan

vLLM

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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": "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

SGLang

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with Ollama:
```
ollama run hf.co/ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF
```

Unsloth Studio

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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 ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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 ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF to start chatting

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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": "ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-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 ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF
```

Lemonade

How to use ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

Run and chat with the model

lemonade run user.Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Experimental global target bits‑per‑weight quantization of huihui-ai/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated

Using non-standard (forked) LLaMA C++ branch for quantization.
Using a CLI tool to build KLD evaluation and imatrix calibration datasets for GGUF models, sourced from eaddario/imatrix-calibration.
Using dataset sources: tools, math, code, text_en, text_ru.
Using dataset chunks: 1000.
Tensors quantinization F16 instead of BF16, Nvidia Pascal architecture friendly like P100.
Small set of patches added.

Many thanks to Ed Addario for an impressive job.

Quantization comparison

BPW/TGS	SIZE (MiB)	PPL correlation	PPL mean ratio	ΔPPL	Mean KLD	Median KLD	Maximum KLD	99.9% KLD	Mean Δp	RMS Δp
3.000	17574	97.54%	1.099746 ± 0.001198	0.703413 ± 0.008959	0.122940 ± 0.000426	0.066714	9.294135	2.637652	-2.015 ± 0.019 %	10.272 ± 0.039 %
3.250	19038	98.25%	1.064443 ± 0.000969	0.454454 ± 0.006978	0.089130 ± 0.000308	0.050624	10.323897	1.992209	-1.732 ± 0.016 %	8.814 ± 0.035 %
3.500	20502	98.69%	1.042289 ± 0.000821	0.298224 ± 0.005835	0.064519 ± 0.000265	0.032550	8.311751	1.765223	-1.217 ± 0.014 %	7.481 ± 0.033 %
3.750	21966	99.04%	1.030411 ± 0.000697	0.214459 ± 0.004989	0.047054 ± 0.000201	0.022846	8.346386	1.258301	-0.654 ± 0.012 %	6.340 ± 0.031 %
4.000	23430	99.15%	1.028018 ± 0.000653	0.197583 ± 0.004668	0.042084 ± 0.000178	0.020843	7.031458	1.095171	-0.639 ± 0.011 %	5.992 ± 0.029 %
4.250	24894	99.25%	1.021574 ± 0.000610	0.152141 ± 0.004327	0.036486 ± 0.000158	0.018297	6.763760	1.009924	-0.568 ± 0.011 %	5.619 ± 0.028 %
4.500	26358	99.50%	1.012304 ± 0.000495	0.086766 ± 0.003506	0.024419 ± 0.000105	0.012078	5.742187	0.643191	-0.204 ± 0.009 %	4.565 ± 0.023 %
4.750	27822	99.61%	1.007470 ± 0.000434	0.052677 ± 0.003063	0.019040 ± 0.000086	0.009403	4.860535	0.504090	-0.200 ± 0.008 %	4.035 ± 0.020 %
5.000	29286	99.67%	1.006065 ± 0.000397	0.042769 ± 0.002799	0.015935 ± 0.000073	0.008021	3.658825	0.425957	-0.201 ± 0.007 %	3.731 ± 0.020 %

Downloads last month: 577

GGUF

Model size

49B params

Architecture

kimi-linear

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

ENOSYS
/

Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

Experimental global target bits‑per‑weight quantization of huihui-ai/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated

Quantization comparison

Dataset used to train ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF