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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("sequelbox/Llama3.1-8B-PlumMath")
model = AutoModelForMultimodalLM.from_pretrained("sequelbox/Llama3.1-8B-PlumMath")
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 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

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	---
	language:
	- en
	pipeline_tag: text-generation
	base_model:
	- meta-llama/Llama-3.1-8B-Instruct
	- ValiantLabs/Llama3.1-8B-ShiningValiant2
	- ValiantLabs/Llama3.1-8B-Cobalt
	library_name: transformers
	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
	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
	license: llama3.1

	---
	# PlumMath

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the della merge method using [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) as a base.

	### Models Merged

	The following models were included in the merge:
	* [ValiantLabs/Llama3.1-8B-ShiningValiant2](https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2)
	* [ValiantLabs/Llama3.1-8B-Cobalt](https://huggingface.co/ValiantLabs/Llama3.1-8B-Cobalt)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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
	```