Instructions to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ControlLLM/Control-LLM-Llama3.1-8B-Math16-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/Control-LLM-Llama3.1-8B-Math16-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct

SGLang

How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-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/Control-LLM-Llama3.1-8B-Math16-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/Control-LLM-Llama3.1-8B-Math16-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/Control-LLM-Llama3.1-8B-Math16-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/Control-LLM-Llama3.1-8B-Math16-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with Docker Model Runner:
```
docker model run hf.co/ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct
```

hawei_LinkedIn commited on Jan 10, 2025

Commit

63e078d

1 Parent(s): 0f70593

update explanation of benchmark result table

Browse files

Files changed (1) hide show

README.md +10 -12

README.md CHANGED Viewed

@@ -86,18 +86,6 @@ The table below summarizes evaluation results across mathematical tasks and orig
 | **Control LLM***   | 36.0   | 61.7   | **89.7**| 62.5      | 82.5    | 30.8     | **71.6**| 45.4     | **57.6**  | **60.0**    |
 ---
-### Catastrophic Forgetting on OpenMath
-The following plot illustrates and compares catastrophic forgetting mitigation during training
-![Catastrophic Forgetting](plots/ControlLLM_CF_Plot_Math.png)
-### Alignment Result
-The plot below highlights the alignment result of the model trained with Control LLM.
-![Alignment](plots/alignment_best.png)
 ### Explanation:
 - **MH**: MathHard
 - **M**: Math
@@ -109,3 +97,13 @@ The plot below highlights the alignment result of the model trained with Control
 - **MLUP**: MMLU Pro
 - **O-Avg**: Original Capability - Average across ARC, GPQA, MMLU, and MLUP
 - **Overall**: Combined average across all tasks

 | **Control LLM***   | 36.0   | 61.7   | **89.7**| 62.5      | 82.5    | 30.8     | **71.6**| 45.4     | **57.6**  | **60.0**    |
 ---
 ### Explanation:
 - **MH**: MathHard
 - **M**: Math
 - **MLUP**: MMLU Pro
 - **O-Avg**: Original Capability - Average across ARC, GPQA, MMLU, and MLUP
 - **Overall**: Combined average across all tasks
+### Catastrophic Forgetting on OpenMath
+The following plot illustrates and compares catastrophic forgetting mitigation during training
+![Catastrophic Forgetting](plots/ControlLLM_CF_Plot_Math.png)
+### Alignment Result
+The plot below highlights the alignment result of the model trained with Control LLM.
+![Alignment](plots/alignment_best.png)