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

Libraries

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

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

pipe = pipeline("text-generation", model="MinimaLabs/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("MinimaLabs/KeyLM-75M-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MinimaLabs/KeyLM-75M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MinimaLabs/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": "MinimaLabs/KeyLM-75M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Eclipse-Senpai commited on May 29

Commit

4811828

verified ·

1 Parent(s): a7de577

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -106,7 +106,7 @@ model-index:
 # KeyLM-75M-Instruct
-KeyLM-75M-Instruct is a 75M parameter instruction-tuned language model trained from scratch on approximately 18 billion tokens. That training budget is a small fraction of what comparable small models use (SmolLM-135M was trained on roughly 600B tokens, SmolLM2-135M on roughly 2T). Despite this, it is competitive on instruction following, outperforming SmolLM-135M-Instruct on IFEval while using about half the parameters and a fraction of the training tokens.
 ## Results

 # KeyLM-75M-Instruct
+KeyLM-75M-Instruct is a 75M parameter instruction-tuned language model trained from scratch on approximately 18 billion tokens. That training budget is a small fraction of what comparable small models use. Despite this, it is competitive on instruction following, outperforming SmolLM-135M-Instruct on IFEval while using about half the parameters and a fraction of the training tokens.
 ## Results