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

File size: 972 Bytes

cca2d4e

{
  "_name_or_path": "/mnt/cache/luzimu/rlhf_math/alignment-handbook/outs/internlm2-20b_ape_th1_169161_gsm8k_math_81087",
  "architectures": [
    "InternLM2ForCausalLM"
  ],
  "attn_implementation": "eager",
  "auto_map": {
    "AutoConfig": "configuration_internlm2.InternLM2Config",
    "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
    "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
  },
  "bias": false,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 6144,
  "initializer_range": 0.02,
  "intermediate_size": 16384,
  "max_position_embeddings": 32768,
  "model_type": "internlm2",
  "num_attention_heads": 48,
  "num_hidden_layers": 48,
  "num_key_value_heads": 8,
  "pad_token_id": 2,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 1000000,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.38.2",
  "use_cache": true,
  "vocab_size": 92672
}