Instructions to use interview-eval/zephyr-7b-math-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use interview-eval/zephyr-7b-math-train with Transformers:

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

pipe = pipeline("text-generation", model="interview-eval/zephyr-7b-math-train")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("interview-eval/zephyr-7b-math-train")
model = AutoModelForCausalLM.from_pretrained("interview-eval/zephyr-7b-math-train")
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

vLLM

How to use interview-eval/zephyr-7b-math-train with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "interview-eval/zephyr-7b-math-train"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "interview-eval/zephyr-7b-math-train",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/interview-eval/zephyr-7b-math-train

SGLang

How to use interview-eval/zephyr-7b-math-train 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 "interview-eval/zephyr-7b-math-train" \
    --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": "interview-eval/zephyr-7b-math-train",
		"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 "interview-eval/zephyr-7b-math-train" \
        --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": "interview-eval/zephyr-7b-math-train",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use interview-eval/zephyr-7b-math-train with Docker Model Runner:
```
docker model run hf.co/interview-eval/zephyr-7b-math-train
```

zephyr-7b-math-train / README.md

Juyoung Suk

End of training

1f38b07 verified over 1 year ago

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metadata

library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
  - trl
  - sft
  - generated_from_trainer
datasets:
  - EunsuKim/MATH
model-index:
  - name: zephyr-7b-math-train
    results: []

zephyr-7b-math-train

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the EunsuKim/MATH dataset. It achieves the following results on the evaluation set:

Loss: 0.0188

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
0.8757	1.0	5	0.7950
0.6949	2.0	10	0.5316
0.48	3.0	15	0.3425
0.2951	4.0	20	0.1809
0.1534	5.0	25	0.0872
0.0746	6.0	30	0.0426
0.0409	7.0	35	0.0291
0.0287	8.0	40	0.0229
0.022	9.0	45	0.0196
0.019	10.0	50	0.0188

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu124
Datasets 2.21.0
Tokenizers 0.19.1