Instructions to use freakyskittle/kimi-k2.75-code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use freakyskittle/kimi-k2.75-code-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="freakyskittle/kimi-k2.75-code-GGUF",
	filename="deep55/k2-merged-pruned-deep55.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use freakyskittle/kimi-k2.75-code-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 freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf freakyskittle/kimi-k2.75-code-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 freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M

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 freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M

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 freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M

Use Docker

docker model run hf.co/freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use freakyskittle/kimi-k2.75-code-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "freakyskittle/kimi-k2.75-code-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "freakyskittle/kimi-k2.75-code-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M

Ollama
How to use freakyskittle/kimi-k2.75-code-GGUF with Ollama:
```
ollama run hf.co/freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M
```

Unsloth Studio

How to use freakyskittle/kimi-k2.75-code-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 freakyskittle/kimi-k2.75-code-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 freakyskittle/kimi-k2.75-code-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for freakyskittle/kimi-k2.75-code-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use freakyskittle/kimi-k2.75-code-GGUF with Docker Model Runner:
```
docker model run hf.co/freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M
```

Lemonade

How to use freakyskittle/kimi-k2.75-code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull freakyskittle/kimi-k2.75-code-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.kimi-k2.75-code-GGUF-Q4_K_M

List all available models

lemonade list

Kimi K2.75 Code — GGUF

GGUF conversions and quantizations of the experimental merged checkpoint freakyskittle/kimi-k2.75-code (a shard-wise SLERP merge of moonshotai/Kimi-K2.7-Code and moonshotai/Kimi-K2.6, DeepSeek-V3 style MoE).

See the base repository for the full merge recipe, pruning details, and license.

Parameter count: ~720 B. The bf16 and Q8_0 builds contain 1,096 tensors summing to 719.8 B parameters. Hugging Face's auto-detected badge reads ~61 B because it parses the deep55/ file first, and that build stores weights in a packed-int4 layout (int32 weight_packed tensors holding 8 weights each, plus weight_scale/weight_shape) whose logical shapes can't be summed. The true model is ~720 B params (further reduced in the deep-pruned deep55/ variant).

Files

Each variant lives in its own folder. Files over Hugging Face's 500 GB per-file limit are split into GGUF shards (-0000N-of-0000M.gguf); point your loader at the first shard and the rest are picked up automatically.

Folder	Variant	Approx. size	Notes
`deep55/`	Deep-pruned, full precision	~203 GB	Prune ratio `0.55`, `deepseek_v3` arch override
`pruned-compact-oxidize-q4/`	Q4_K_M (oxidize)	~406 GB	Quantized from the compacted pruned checkpoint
`llamacpp-q4-partial/`	Q4_K_M (llama.cpp, partial)	~435 GB	Partial llama.cpp quantization
`unpruned-q8/`	Q8_0 (unpruned)	~765 GB	Split into 2 shards
`pruned-bf16/`	BF16 (pruned)	~1.44 TB	Split into 4 shards

Provenance

Base merge + pruning: see freakyskittle/kimi-k2.75-code.
GGUF conversion via oxidize-convert with --arch deepseek_v3.
Sharding via llama-gguf-split (--split-max-size 450G).

Status

Experimental research artifact — not fully evaluated. Validate quality before any production use. Use at your own risk.

License

Follows the Modified MIT License from Moonshot AI (see the base repository). Commercial attribution requirement applies: products/services exceeding 100M monthly active users or US$20M monthly revenue must prominently display Kimi K2.7 Code in the UI.

Downloads last month: 3,481

GGUF

Model size

62B params

Architecture

deepseek_v3

Hardware compatibility

4-bit

8-bit

View +1 variant

Model tree for freakyskittle/kimi-k2.75-code-GGUF

freakyskittle/kimi-k2.75-code

moonshotai/Kimi-K2.6

moonshotai/Kimi-K2.7-Code

Merge model

this model