Instructions to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with Transformers:

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

pipe = pipeline("text-generation", model="MathGenie/Mistral-7B-Ours-SFT-SCDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MathGenie/Mistral-7B-Ours-SFT-SCDPO")
model = AutoModelForCausalLM.from_pretrained("MathGenie/Mistral-7B-Ours-SFT-SCDPO", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathGenie/Mistral-7B-Ours-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/Mistral-7B-Ours-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MathGenie/Mistral-7B-Ours-SFT-SCDPO

SGLang

How to use MathGenie/Mistral-7B-Ours-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/Mistral-7B-Ours-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/Mistral-7B-Ours-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/Mistral-7B-Ours-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/Mistral-7B-Ours-SFT-SCDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MathGenie/Mistral-7B-Ours-SFT-SCDPO with Docker Model Runner:
```
docker model run hf.co/MathGenie/Mistral-7B-Ours-SFT-SCDPO
```

Mistral-7B-Ours-SFT-SCDPO / README.md

luzimu

modify readme

f3220f1 about 2 years ago

preview code

Raw History Blame Contribute Delete

3.51 kB

metadata

base_model: MathGenie/Mistral-7B-Ours-SFT
tags:
  - math
model-index:
  - name: Mistral-7B-Ours-SFT-SCDPO
    results: []
license: apache-2.0
language:
  - en
metrics:
  - accuracy
pipeline_tag: text-generation

Mistral-7B-Ours-SFT-SCDPO

This model is a fine-tuned version of MathGenie/Mistral-7B-Ours-SFT. It achieves the following results on the evaluation set:

Loss: 0.1793
Rewards/chosen: 0.2587
Rewards/rejected: -7.0301
Rewards/accuracies: 0.8947
Rewards/margins: 7.2889
Logps/rejected: -253.7773
Logps/chosen: -80.3105
Logits/rejected: -2.3417
Logits/chosen: -2.3846

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

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-07
train_batch_size: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 16
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

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.3963	0.21	100	0.3636	1.8634	-0.1518	0.8816	2.0152	-184.9944	-64.2644	-2.7112	-2.7505
0.2849	0.43	200	0.2598	0.7706	-3.7221	0.8816	4.4927	-220.6974	-75.1921	-2.5067	-2.5475
0.2496	0.64	300	0.2295	0.9323	-4.2717	0.8684	5.2040	-226.1934	-73.5753	-2.5080	-2.5494
0.2331	0.86	400	0.2089	0.7871	-4.8912	0.8684	5.6783	-232.3884	-75.0269	-2.4967	-2.5382
0.0874	1.07	500	0.1872	0.6345	-5.7444	0.8816	6.3789	-240.9202	-76.5527	-2.4323	-2.4761
0.1217	1.28	600	0.1832	0.2282	-6.6907	0.8684	6.9188	-250.3827	-80.6161	-2.3741	-2.4172
0.0966	1.5	700	0.1807	0.1849	-7.0125	0.8816	7.1975	-253.6012	-81.0485	-2.3503	-2.3940
0.0755	1.71	800	0.1802	0.3224	-6.9539	0.8947	7.2763	-253.0150	-79.6739	-2.3437	-2.3867
0.1177	1.93	900	0.1793	0.2587	-7.0301	0.8947	7.2889	-253.7773	-80.3105	-2.3417	-2.3846

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.15.2