Instructions to use CardinalOperations/ORLM-LLaMA-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CardinalOperations/ORLM-LLaMA-3-8B with Transformers:

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

pipe = pipeline("text-generation", model="CardinalOperations/ORLM-LLaMA-3-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CardinalOperations/ORLM-LLaMA-3-8B")
model = AutoModelForCausalLM.from_pretrained("CardinalOperations/ORLM-LLaMA-3-8B")

Inference
Local Apps Settings

vLLM

How to use CardinalOperations/ORLM-LLaMA-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CardinalOperations/ORLM-LLaMA-3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CardinalOperations/ORLM-LLaMA-3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CardinalOperations/ORLM-LLaMA-3-8B

SGLang

How to use CardinalOperations/ORLM-LLaMA-3-8B 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 "CardinalOperations/ORLM-LLaMA-3-8B" \
    --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": "CardinalOperations/ORLM-LLaMA-3-8B",
		"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 "CardinalOperations/ORLM-LLaMA-3-8B" \
        --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": "CardinalOperations/ORLM-LLaMA-3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CardinalOperations/ORLM-LLaMA-3-8B with Docker Model Runner:
```
docker model run hf.co/CardinalOperations/ORLM-LLaMA-3-8B
```

Add library_name and pipeline_tag, correct license

by nielsr HF Staff - opened Apr 7, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +6 -3

README.md CHANGED Viewed

@@ -1,6 +1,9 @@
 ---
 license: llama3
 ---
 see our paper in https://arxiv.org/abs/2405.17743
 github repo: https://github.com/Cardinal-Operations/ORLM
@@ -31,7 +34,7 @@ Please replace the `{Question}` with any natural language OR question.
 Below is an operations research question. Build a mathematical model and corresponding python code using `coptpy` that appropriately addresses the question.
 # Question:
-A lab has 1000 units of medicinal ingredients to make two pills, a large pill and a small pill. A large pill requires 3 units of medicinal ingredients and 2 units of filler. A small pill requires 2 units of medicinal ingredients and 1 unit of filler. The lab has to make at least 100 large pills. However, since small pills are more popular at least 60% of the total number of pills must be small. How many of each should be made to minimize the total number of filler material needed?
 # Response:
 --------------------------top-1 completion output by LLaMA-3-8B-ORLM under greedy decoding--------------------------
@@ -59,7 +62,7 @@ Using this model, we can apply linear programming techniques to find the optimal
 ## Python Code Solution Using `coptpy`:
 Here is a Python script using the `coptpy` library to solve the problem:
-\`\`\`python
 import coptpy as cp
 from coptpy import COPT
@@ -91,7 +94,7 @@ if model.status == COPT.OPTIMAL:
     print("Number of small pills to be made: {:.0f}".format(y.x))
 else:
     print("No optimal solution found.")
-\`\`\`
 In this script, we first create a `COPT` environment and model. Then, we add two integer decision variables `x` and `y`, representing the number of large and small pills to be made, respectively.

 ---
 license: llama3
+library_name: transformers
+pipeline_tag: text-generation
 ---
 see our paper in https://arxiv.org/abs/2405.17743
 github repo: https://github.com/Cardinal-Operations/ORLM
 Below is an operations research question. Build a mathematical model and corresponding python code using `coptpy` that appropriately addresses the question.
 # Question:
+A lab has 1000 units of medicinal ingredients to make two pills, a large pill and a small pill. A large pill requires 3 units of medicinal ingredients and 2 units of filler. A large pill requires 3 units of medicinal ingredients and 2 units of filler. A small pill requires 2 units of medicinal ingredients and 1 unit of filler. The lab has to make at least 100 large pills. However, since small pills are more popular at least 60% of the total number of pills must be small. How many of each should be made to minimize the total number of filler material needed?
 # Response:
 --------------------------top-1 completion output by LLaMA-3-8B-ORLM under greedy decoding--------------------------
 ## Python Code Solution Using `coptpy`:
 Here is a Python script using the `coptpy` library to solve the problem:
+```python
 import coptpy as cp
 from coptpy import COPT
     print("Number of small pills to be made: {:.0f}".format(y.x))
 else:
     print("No optimal solution found.")
+```
 In this script, we first create a `COPT` environment and model. Then, we add two integer decision variables `x` and `y`, representing the number of large and small pills to be made, respectively.