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

Libraries

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

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

pipe = pipeline("text-generation", model="freakyskittle/kimi-k2.75-code", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("freakyskittle/kimi-k2.75-code", trust_remote_code=True)
model = AutoModel.from_pretrained("freakyskittle/kimi-k2.75-code", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use freakyskittle/kimi-k2.75-code 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use freakyskittle/kimi-k2.75-code 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 "freakyskittle/kimi-k2.75-code" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "freakyskittle/kimi-k2.75-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "freakyskittle/kimi-k2.75-code" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "freakyskittle/kimi-k2.75-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

freakyskittle commited on Jun 16

Commit

f6ce165

verified ·

1 Parent(s): 43d8910

Add README.md with Modified MIT license details

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README.md +6 -0

README.md CHANGED Viewed

@@ -40,6 +40,12 @@ The uploaded safetensors are the compacted pruned checkpoint:
 - Final routed experts per MoE layer: `268`
 - Safetensors checkpoint size: about `394G`
 ## Status
 This model is **not fully evaluated yet**. Treat it as a research artifact.

 - Final routed experts per MoE layer: `268`
 - Safetensors checkpoint size: about `394G`
+## License
+This checkpoint follows the Modified MIT License from Moonshot AI. See [`LICENSE`](./LICENSE).
+Commercial attribution requirement: if the Software or derivative works are used for commercial products or services with more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you must prominently display `Kimi K2.7 Code` on the user interface of such product or service.
 ## Status
 This model is **not fully evaluated yet**. Treat it as a research artifact.