Image-Text-to-Text
Transformers
Safetensors
gsq
gumbel-softmax
quantization
ptq
Mixture of Experts
kimi
vllm
humming
Instructions to use ISTA-DASLab/Kimi-K2.5-2Bit-GSQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISTA-DASLab/Kimi-K2.5-2Bit-GSQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ISTA-DASLab/Kimi-K2.5-2Bit-GSQ")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ISTA-DASLab/Kimi-K2.5-2Bit-GSQ", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ISTA-DASLab/Kimi-K2.5-2Bit-GSQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ISTA-DASLab/Kimi-K2.5-2Bit-GSQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ISTA-DASLab/Kimi-K2.5-2Bit-GSQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ISTA-DASLab/Kimi-K2.5-2Bit-GSQ
- SGLang
How to use ISTA-DASLab/Kimi-K2.5-2Bit-GSQ 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 "ISTA-DASLab/Kimi-K2.5-2Bit-GSQ" \ --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": "ISTA-DASLab/Kimi-K2.5-2Bit-GSQ", "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 "ISTA-DASLab/Kimi-K2.5-2Bit-GSQ" \ --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": "ISTA-DASLab/Kimi-K2.5-2Bit-GSQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ISTA-DASLab/Kimi-K2.5-2Bit-GSQ with Docker Model Runner:
docker model run hf.co/ISTA-DASLab/Kimi-K2.5-2Bit-GSQ
Add humming instructions
Browse files
README.md
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Loading this checkpoint requires
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> Note: GSQ training first writes shards in `compressed-tensors`
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## Serving with vLLM
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Hopper (sm_90) or Ampere (sm ≥ 80) GPUs required for serving. On 8× H100/H200,
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valid TP sizes are `1, 2, 4, 8` (Marlin MoE constraint with group size 128).
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}
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```
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Loading this checkpoint requires vLLM plus the
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[`humming`](https://github.com/inclusionAI/humming) MoE kernels (`pip install
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humming-kernels`). See **Serving with vLLM** below.
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> Note: GSQ training first writes shards in `compressed-tensors`
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## Serving with vLLM
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Install the Humming kernels (required for vLLM to load this checkpoint):
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```bash
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pip install humming-kernels
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```
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Hopper (sm_90) or Ampere (sm ≥ 80) GPUs required for serving. On 8× H100/H200,
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valid TP sizes are `1, 2, 4, 8` (Marlin MoE constraint with group size 128).
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