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 Settings

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 commited on Jan 10, 2025

Commit

07189e4

verified ·

1 Parent(s): 0d94b52

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@@ -90,15 +90,15 @@ The plot below highlights the alignment comparison of the model trained with Con
 ### Benchmark Results Table
 The table below summarizes the evaluation results across mathematical tasks and original capabilities for various models and training approaches.
-| **Model**                | **Math Tasks**             |          |           |          | **Original Capabilities**   |         |         |           | **Overall Avg.** |
-|                          | **MathHard**              | **Math** | **GSM8K** | **Avg.** | **ARC**                     | **GPQA**| **MMLU**| **MMLUP** |                  |
-| Llama3.1-8B-Instruct     | 23.7                      | 50.9     | 85.6      | 52.1     | 83.4                        | 29.9    | 72.4    | 46.7      | 56.3             |
-| OpenMath2-Llama3.1       | 38.4                      | 64.1     | 90.3      | 64.3     | 45.8                        | 1.3     | 4.5     | 19.5      | 38.6             |
-| **Full Param Tune**       | **38.5**                  | **63.7** | 90.2      | **63.9** | 58.2                        | 1.1     | 7.3     | 23.5      | 40.1             |
-| Partial Param Tune       | 36.4                      | 61.4     | 89.0      | 61.8     | 66.2                        | 6.0     | 25.7    | 30.9      | 45.6             |
-| Stack Expansion          | 35.6                      | 61.0     | 90.8      | 61.8     | 69.3                        | 18.8    | 61.8    | 43.1      | 57.6             |
-| Hybrid Expansion         | 34.4                      | 61.1     | 90.1      | 61.5     | **81.8**                    | **25.9**| 67.2    | **43.9**  | 59.3             |
-| **Control LLM***          | 38.1                      | 62.7     | **90.4**  | 63.2     | 79.7                        | 25.2    | **68.1**| 43.6      | **60.2**         |
 ---

 ### Benchmark Results Table
 The table below summarizes the evaluation results across mathematical tasks and original capabilities for various models and training approaches.
+| **Model**                | **MathHard** | **Math** | **GSM8K** | **Avg. (Math)** | **ARC** | **GPQA** | **MMLU** | **MMLUP** | **Avg. (Original)** | **Overall Avg.** |
+|--------------------------|--------------|----------|-----------|-----------------|---------|----------|----------|-----------|---------------------|------------------|
+| Llama3.1-8B-Instruct     | 23.7         | 50.9     | 85.6      | 52.1            | 83.4    | 29.9     | 72.4     | 46.7      | 60.5                | 56.3             |
+| OpenMath2-Llama3.1       | 38.4         | 64.1     | 90.3      | 64.3            | 45.8    | 1.3      | 4.5      | 19.5      | 12.9                | 38.6             |
+| **Full Param Tune**       | **38.5**     | **63.7** | 90.2      | **63.9**        | 58.2    | 1.1      | 7.3      | 23.5      | 16.5                | 40.1             |
+| Partial Param Tune       | 36.4         | 61.4     | 89.0      | 61.8            | 66.2    | 6.0      | 25.7     | 30.9      | 29.3                | 45.6             |
+| Stack Expansion          | 35.6         | 61.0     | 90.8      | 61.8            | 69.3    | 18.8     | 61.8     | 43.1      | 53.3                | 57.6             |
+| Hybrid Expansion         | 34.4         | 61.1     | 90.1      | 61.5            | **81.8**| **25.9** | 67.2     | **43.9**  | 57.1                | 59.3             |
+| **Control LLM***          | 38.1         | 62.7     | **90.4**  | 63.2            | 79.7    | 25.2     | **68.1** | 43.6      | **57.2**            | **60.2**         |
 ---