Instructions to use sequelbox/Llama3.1-8B-PlumMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sequelbox/Llama3.1-8B-PlumMath with Transformers:

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

pipe = pipeline("text-generation", model="sequelbox/Llama3.1-8B-PlumMath")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sequelbox/Llama3.1-8B-PlumMath")
model = AutoModelForCausalLM.from_pretrained("sequelbox/Llama3.1-8B-PlumMath", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sequelbox/Llama3.1-8B-PlumMath with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sequelbox/Llama3.1-8B-PlumMath"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sequelbox/Llama3.1-8B-PlumMath",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sequelbox/Llama3.1-8B-PlumMath

SGLang

How to use sequelbox/Llama3.1-8B-PlumMath 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 "sequelbox/Llama3.1-8B-PlumMath" \
    --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": "sequelbox/Llama3.1-8B-PlumMath",
		"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 "sequelbox/Llama3.1-8B-PlumMath" \
        --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": "sequelbox/Llama3.1-8B-PlumMath",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sequelbox/Llama3.1-8B-PlumMath with Docker Model Runner:
```
docker model run hf.co/sequelbox/Llama3.1-8B-PlumMath
```

Llama3.1-8B-PlumMath / README.md

sequelbox

Adding Evaluation Results (#2)

bfaa2e7 verified about 2 years ago

preview code

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metadata

language:
  - en
license: llama3.1
library_name: transformers
tags:
  - mergekit
  - merge
  - shining-valiant
  - shining-valiant-2
  - cobalt
  - plum
  - valiant
  - valiant-labs
  - llama
  - llama-3.1
  - llama-3.1-instruct
  - llama-3.1-instruct-8b
  - llama-3
  - llama-3-instruct
  - llama-3-instruct-8b
  - 8b
  - math
  - math-instruct
  - science
  - physics
  - biology
  - chemistry
  - compsci
  - computer-science
  - engineering
  - technical
  - conversational
  - chat
  - instruct
base_model:
  - meta-llama/Llama-3.1-8B-Instruct
  - ValiantLabs/Llama3.1-8B-ShiningValiant2
  - ValiantLabs/Llama3.1-8B-Cobalt
pipeline_tag: text-generation
model_type: llama
model-index:
  - name: sequelbox/Llama3.1-8B-PlumMath
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-Shot)
          type: Winogrande
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 72.38
            name: acc
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MathQA (5-Shot)
          type: MathQA
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 40.27
            name: acc
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 22.42
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumMath
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 16.45
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumMath
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 3.93
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumMath
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 9.06
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumMath
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 8.98
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumMath
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 21.95
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumMath
          name: Open LLM Leaderboard

PlumMath

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the della merge method using meta-llama/Llama-3.1-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: della
dtype: bfloat16
parameters:
  normalize: true
models:
  - model: ValiantLabs/Llama3.1-8B-ShiningValiant2
    parameters:
      density: 0.5
      weight: 0.3
  - model: ValiantLabs/Llama3.1-8B-Cobalt
    parameters:
      density: 0.5
      weight: 0.2
base_model: meta-llama/Llama-3.1-8B-Instruct

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	13.80
IFEval (0-Shot)	22.42
BBH (3-Shot)	16.45
MATH Lvl 5 (4-Shot)	3.93
GPQA (0-shot)	9.06
MuSR (0-shot)	8.98
MMLU-PRO (5-shot)	21.95