Instructions to use Eclipse-Senpai/KeyLM-75M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Eclipse-Senpai/KeyLM-75M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Eclipse-Senpai/KeyLM-75M-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Eclipse-Senpai/KeyLM-75M-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Eclipse-Senpai/KeyLM-75M-Instruct 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-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Eclipse-Senpai/KeyLM-75M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct

SGLang

How to use Eclipse-Senpai/KeyLM-75M-Instruct 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-Instruct" \
    --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": "Eclipse-Senpai/KeyLM-75M-Instruct",
		"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 "Eclipse-Senpai/KeyLM-75M-Instruct" \
        --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": "Eclipse-Senpai/KeyLM-75M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Eclipse-Senpai/KeyLM-75M-Instruct with Docker Model Runner:
```
docker model run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct
```

KeyLM-75M-Instruct / modeling_keylm.py

Eclipse-Senpai

main commit

a7de577 verified 2 days ago

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	"""KeyLM model implementation.

	KeyLM-75M uses a Qwen3-style decoder (GQA + RoPE + SwiGLU + per-head
	QK-RMSNorm). Rather than vendor a full copy of the transformer, the classes
	below specialise the upstream Qwen3 implementation and bind it to KeyLMConfig
	so the model loads under its own name via `trust_remote_code=True`.
	"""

	try:
	from transformers.models.qwen3.modeling_qwen3 import Qwen3ForCausalLM, Qwen3Model
	except ImportError as exc: # pragma: no cover - guidance for old transformers
	raise ImportError(
	"KeyLM requires a transformers version that ships the Qwen3 model "
	"(transformers>=4.51). Please upgrade transformers."
	) from exc

	from .configuration_keylm import KeyLM75MConfig


	class KeyLM75MModel(Qwen3Model):
	config_class = KeyLM75MConfig


	class KeyLM75M(Qwen3ForCausalLM):
	config_class = KeyLM75MConfig