Instructions to use MathGenie/InternLM2-SFT-SCDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathGenie/InternLM2-SFT-SCDPO with Transformers:

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

pipe = pipeline("text-generation", model="MathGenie/InternLM2-SFT-SCDPO", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MathGenie/InternLM2-SFT-SCDPO", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MathGenie/InternLM2-SFT-SCDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathGenie/InternLM2-SFT-SCDPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/InternLM2-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MathGenie/InternLM2-SFT-SCDPO

SGLang

How to use MathGenie/InternLM2-SFT-SCDPO 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 "MathGenie/InternLM2-SFT-SCDPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/InternLM2-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "MathGenie/InternLM2-SFT-SCDPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathGenie/InternLM2-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MathGenie/InternLM2-SFT-SCDPO with Docker Model Runner:
```
docker model run hf.co/MathGenie/InternLM2-SFT-SCDPO
```

InternLM2-SFT-SCDPO / README.md

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	---
	base_model: InternLM2-SFT
	tags:
	- math
	model-index:
	- name: InternLM2-SFT-SCDPO
	results: []
	license: apache-2.0
	language:
	- en
	metrics:
	- accuracy
	pipeline_tag: text-generation
	---

	# InternLM2-SFT-SCDPO

	This model is a fine-tuned version of the InternLM2-20B model using SFT and SCDPO.
	It achieves the following results on the evaluation set:

	- Loss: 0.2572
	- Rewards/chosen: 0.7366
	- Rewards/rejected: -2.9817
	- Rewards/accuracies: 0.8929
	- Rewards/margins: 3.7183
	- Logps/rejected: -155.1884
	- Logps/chosen: -92.5904
	- Logits/rejected: -2.3032
	- Logits/chosen: -2.4880

	## Model description

	This is a model fine-tuned for mathematical problem-solving.

	## Intended uses & limitations

	The model is intended for solving math problems.

	## Training and evaluation data

	\| \| gsm8k \| math \| ape \| cmath \| mgsm_zh \|
	\| ------------------------------ \| -------- \| -------- \| -------- \| -------- \| -------- \|
	\| InternLM2-SFT \| 86.4 \| 55.8 \| 77.1 \| 88.4 \| 74.8 \|
	\| InternLM2-SFT-DPO \| 87 \| 57.6 \| 78.7 \| 89.9 \| 76 \|
	\| InternLM2-SFT-DPO (data-equal) \| 88.2 \| 57.5 \| 78.8 \| 89.3 \| 76 \|
	\| InternLM2-SFT-SCDPO \| 88.5 \| 58.1 \| 79.3 \| 90.3 \| 80.4 \|



	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 1.5e-07
	- train_batch_size: 2
	- eval_batch_size: 4
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 16
	- total_train_batch_size: 32
	- total_eval_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 2

	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.1.2
	- Datasets 2.14.6
	- Tokenizers 0.15.2