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 9, 2025

Commit

50abae0

verified ·

1 Parent(s): 1549e51

Update model card with metrics

Browse files

Files changed (1) hide show

README.md +71 -2

README.md CHANGED Viewed

@@ -1,2 +1,71 @@
-# Control-LLM-Llama3.1-8B-Math16
-This is a fine-tuned model of Llama-3.1-8B-Instruct for mathematical tasks on OpenMath2 dataset.

+---
+license: llama3.1
+datasets:
+- nvidia/OpenMathInstruct-2
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+model-index:
+- name: Control-LLM-Llama3.1-8B-Math16
+  results:
+  - task:
+      type: math-evaluation
+    dataset:
+      type: nvidia/OpenMathInstruct-2
+      name: OpenMathInstruct
+    metrics:
+    - name: exact_match,none
+      type: exact_match
+      value: 0.6327358367133324
+      stderr: 0.0052245703347459605
+      verified: false
+    - name: exact_match,none (gsm8k_0shot_instruct)
+      type: exact_match
+      value: 0.9052312357846853
+      stderr: 0.008067791560015407
+      verified: false
+    - name: exact_match,none (meta_math_0shot_instruct)
+      type: exact_match
+      value: 0.6276
+      stderr: 0.006837616441401548
+      verified: false
+    - name: exact_match,none (meta_math_hard_0shot_instruct)
+      type: exact_match
+      value: 0.3806646525679758
+      stderr: 0.013349170720370741
+      verified: false
+  - task:
+      type: original-capability
+    dataset:
+      type: meta/arc-dataset
+      name: Meta-ARC Dataset
+    metrics:
+    - name: exact_match,strict-match
+      type: exact_match
+      value: 0.5723263625528227
+      stderr: 0.002858377993520894
+      verified: false
+    - name: exact_match,strict-match (meta_arc_0shot_instruct)
+      type: exact_match
+      value: 0.7974248927038626
+      stderr: 0.01178043813618557
+      verified: false
+    - name: exact_match,strict-match (meta_gpqa_0shot_cot_instruct)
+      type: exact_match
+      value: 0.25223214285714285
+      stderr: 0.02054139101648797
+      verified: false
+    - name: exact_match,strict-match (meta_mmlu_0shot_instruct)
+      type: exact_match
+      value: 0.6837345107534539
+      stderr: 0.0039243761987253515
+      verified: false
+    - name: exact_match,strict-match (meta_mmlu_pro_5shot_instruct)
+      type: exact_match
+      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.