Instructions to use Q-bert/MetaMath-Cybertron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Q-bert/MetaMath-Cybertron with Transformers:

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

pipe = pipeline("text-generation", model="Q-bert/MetaMath-Cybertron")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Q-bert/MetaMath-Cybertron")
model = AutoModelForCausalLM.from_pretrained("Q-bert/MetaMath-Cybertron")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Q-bert/MetaMath-Cybertron with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Q-bert/MetaMath-Cybertron"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Q-bert/MetaMath-Cybertron",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Q-bert/MetaMath-Cybertron

SGLang

How to use Q-bert/MetaMath-Cybertron 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 "Q-bert/MetaMath-Cybertron" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Q-bert/MetaMath-Cybertron",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Q-bert/MetaMath-Cybertron" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Q-bert/MetaMath-Cybertron",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Q-bert/MetaMath-Cybertron with Docker Model Runner:
```
docker model run hf.co/Q-bert/MetaMath-Cybertron
```

MetaMath-Cybertron / README.md

leaderboard-pr-bot

Adding Evaluation Results

9f64d13 verified about 2 years ago

4.38 kB

language:
  - en
license: apache-2.0
tags:
  - Math
datasets:
  - meta-math/MetaMathQA
pipeline_tag: text-generation
base_model:
  - fblgit/una-cybertron-7b-v2-bf16
  - meta-math/MetaMath-Mistral-7B
model-index:
  - name: MetaMath-Cybertron
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 66.47
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 85.54
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 63.71
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 57.71
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 79.64
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 70.51
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
          name: Open LLM Leaderboard

MetaMath-Cybertron

Merge fblgit/una-cybertron-7b-v2-bf16 and meta-math/MetaMath-Mistral-7B using slerp merge.

You can use ChatML format.

Open LLM Leaderboard Evaluation Results

Detailed results can be found Coming soon

Metric	Value
Avg.	Coming soon
ARC (25-shot)	Coming soon
HellaSwag (10-shot)	Coming soon
MMLU (5-shot)	Coming soon
TruthfulQA (0-shot)	Coming soon
Winogrande (5-shot)	Coming soon
GSM8K (5-shot)	Coming soon

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	70.60
AI2 Reasoning Challenge (25-Shot)	66.47
HellaSwag (10-Shot)	85.54
MMLU (5-Shot)	63.71
TruthfulQA (0-shot)	57.71
Winogrande (5-shot)	79.64
GSM8k (5-shot)	70.51