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, dtype="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
```

MathGenie commited on Jun 29, 2024

Commit

32f8c56

verified ·

1 Parent(s): cca2d4e

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -1,16 +1,18 @@
 ---
 base_model: InternLM2-SFT
 tags:
-- alignment-handbook
-- generated_from_trainer
 model-index:
 - name: InternLM2-SFT-SCDPO
   results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 # InternLM2-SFT-SCDPO
 This model is a fine-tuned version of the InternLM2-20B model using SFT and SCDPO.
@@ -129,4 +131,4 @@ The following hyperparameters were used during training:
 - Transformers 4.38.2
 - Pytorch 2.1.2
 - Datasets 2.14.6
-- Tokenizers 0.15.2

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