Instructions to use NoesisLab/Geilim-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NoesisLab/Geilim-1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="NoesisLab/Geilim-1B-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("NoesisLab/Geilim-1B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NoesisLab/Geilim-1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NoesisLab/Geilim-1B-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": "NoesisLab/Geilim-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NoesisLab/Geilim-1B-Instruct

SGLang

How to use NoesisLab/Geilim-1B-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 "NoesisLab/Geilim-1B-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": "NoesisLab/Geilim-1B-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 "NoesisLab/Geilim-1B-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": "NoesisLab/Geilim-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NoesisLab/Geilim-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/NoesisLab/Geilim-1B-Instruct
```

OzTianlu commited on Jan 29

Commit

c6149cb

verified ·

1 Parent(s): 36b6364

Upload README.md

Browse files

Files changed (1) hide show

README.md +0 -12

README.md CHANGED Viewed

@@ -286,18 +286,6 @@ Geilim is evaluated on:
 - **Response conciseness** (< 150 chars = concise)
 - **Reasoning traces** (should be absent from output, present in hidden states)
-### Test Script
-```bash
-python test_geilim.py
-```
-Compares Geilim vs Llama-3.2-1B-Instruct baseline on 8 reasoning tasks.
-### Run Benchmarks
-```bash
-python run_lmeval.py
-```
-Evaluates on: WinoGrande, ARC (easy/challenge), HellaSwag, PIQA.
 ---
 ## 🎯 Use Cases

 - **Response conciseness** (< 150 chars = concise)
 - **Reasoning traces** (should be absent from output, present in hidden states)
 ---
 ## 🎯 Use Cases