Instructions to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ControlLLM/Llama3.1-8B-OpenMath16-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ControlLLM/Llama3.1-8B-OpenMath16-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ControlLLM/Llama3.1-8B-OpenMath16-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ControlLLM/Llama3.1-8B-OpenMath16-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ControlLLM/Llama3.1-8B-OpenMath16-Instruct

SGLang

How to use ControlLLM/Llama3.1-8B-OpenMath16-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 "ControlLLM/Llama3.1-8B-OpenMath16-Instruct" \
    --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": "ControlLLM/Llama3.1-8B-OpenMath16-Instruct",
		"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 "ControlLLM/Llama3.1-8B-OpenMath16-Instruct" \
        --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": "ControlLLM/Llama3.1-8B-OpenMath16-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with Docker Model Runner:
```
docker model run hf.co/ControlLLM/Llama3.1-8B-OpenMath16-Instruct
```

hawei

nielsr HF Staff commited on Jan 25, 2025

Commit

f2c2ff4

verified ·

1 Parent(s): c088c6c

Add library name, pipeline tag, and link to paper (#1)

Browse files

- Add library name, pipeline tag, and link to paper (e530760aa1363894529669bbaef8f4bbf7cb46a9)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +6 -1

README.md CHANGED Viewed

@@ -4,6 +4,8 @@ datasets:
 - nvidia/OpenMathInstruct-2
 language:
 - en
 base_model:
 - meta-llama/Llama-3.1-8B-Instruct
 model-index:
@@ -70,9 +72,12 @@ model-index:
       value: 0.4324301861702128
       stderr: 0.004516653585262379
       verified: false
 ---
 # Control-LLM-Llama3.1-8B-Math16
-This is a fine-tuned model of Llama-3.1-8B-Instruct for mathematical tasks on OpenMath2 dataset.
 ## Linked Paper
 This model is associated with the paper: [Control-LLM](https://arxiv.org/abs/2501.10979).

 - nvidia/OpenMathInstruct-2
 language:
 - en
+metrics:
+- accuracy
 base_model:
 - meta-llama/Llama-3.1-8B-Instruct
 model-index:
       value: 0.4324301861702128
       stderr: 0.004516653585262379
       verified: false
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # Control-LLM-Llama3.1-8B-Math16
+This is a fine-tuned model of Llama-3.1-8B-Instruct for mathematical tasks on OpenMath2 dataset, as described in the paper [Control LLM: Controlled Evolution for Intelligence Retention in LLM](https://huggingface.co/papers/2501.10979).
 ## Linked Paper
 This model is associated with the paper: [Control-LLM](https://arxiv.org/abs/2501.10979).