Text Generation
Transformers
Safetensors
English
keylm75m
keylm
small-language-model
base
pretrained
gqa
rope
swiglu
qk-norm
custom_code
Instructions to use Eclipse-Senpai/KeyLM-75M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eclipse-Senpai/KeyLM-75M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Eclipse-Senpai/KeyLM-75M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Eclipse-Senpai/KeyLM-75M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Eclipse-Senpai/KeyLM-75M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Eclipse-Senpai/KeyLM-75M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Eclipse-Senpai/KeyLM-75M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Eclipse-Senpai/KeyLM-75M
- SGLang
How to use Eclipse-Senpai/KeyLM-75M 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 "Eclipse-Senpai/KeyLM-75M" \ --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": "Eclipse-Senpai/KeyLM-75M", "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 "Eclipse-Senpai/KeyLM-75M" \ --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": "Eclipse-Senpai/KeyLM-75M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Eclipse-Senpai/KeyLM-75M with Docker Model Runner:
docker model run hf.co/Eclipse-Senpai/KeyLM-75M
| """KeyLM model configuration. | |
| KeyLM-75M is a from-scratch small language model. Its decoder block is a | |
| Qwen3-style layout (grouped-query attention, RoPE, SwiGLU, and per-head | |
| QK-RMSNorm), so the configuration inherits Qwen3Config and only overrides the | |
| ``model_type`` so the model carries its own identity on the Hub. | |
| """ | |
| from transformers.models.qwen3.configuration_qwen3 import Qwen3Config | |
| class KeyLM75MConfig(Qwen3Config): | |
| model_type = "keylm75m" | |