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

36b6364

verified ·

1 Parent(s): 10bc31b

Upload README.md

Browse files

Files changed (1) hide show

README.md +23 -2

README.md CHANGED Viewed

@@ -1,3 +1,26 @@
 # Geilim-1B-Instruct (忌廉)
 > **Deep Causal Internal Reasoning**
@@ -384,8 +407,6 @@ Llama 3.2 Community License
 ## 🔗 Links
 - **Model Hub**: https://huggingface.co/NoesisLab/Geilim-1B-Instruct
-- **Repository**: https://github.com/Liuxingyu1111111/Asterisk-R1
 ---
 **Built with ❤️ for the era of efficient reasoning models.**

+---
+library_name: transformers
+language:
+- en
+tags:
+- reasoning
+- implicit-reasoning
+- chain-of-thought
+- llama
+- asterisk
+- aspp
+- pi-flow
+- deep-reasoning
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B-Instruct
+model_name: Geilim-1B-Instruct
+datasets:
+- gsm8k
+- hellaswag
+- ai2_arc
+pipeline_tag: text-generation
+---
 # Geilim-1B-Instruct (忌廉)
 > **Deep Causal Internal Reasoning**
 ## 🔗 Links
 - **Model Hub**: https://huggingface.co/NoesisLab/Geilim-1B-Instruct
 ---
 **Built with ❤️ for the era of efficient reasoning models.**