Instructions to use Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math with Transformers:

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

pipe = pipeline("text-generation", model="Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math")
model = AutoModelForCausalLM.from_pretrained("Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math

SGLang

How to use Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math 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 "Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math" \
    --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": "Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math",
		"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 "Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math" \
        --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": "Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math with Docker Model Runner:
```
docker model run hf.co/Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math
```

Llama3.1-8B-Base-Arcee-Code-Math

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

Merge Details

Merge Method

This model was merged using the Arcee Fusion merge method using Alelcv27/Llama3.1-8B-Base-Code as a base.

Models Merged

The following models were included in the merge:

Alelcv27/Llama3.1-8B-Base-Math

Configuration

The following YAML configuration was used to produce this model:

base_model: Alelcv27/Llama3.1-8B-Base-Code
dtype: bfloat16
merge_method: arcee_fusion
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 32]
        model: Alelcv27/Llama3.1-8B-Base-Math
      - layer_range: [0, 32]
        model: Alelcv27/Llama3.1-8B-Base-Code

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Safetensors

Model size

8B params

Tensor type

F32

Model tree for Alelcv27/Llama3.1-8B-Base-Arcee-Code-Math

Alelcv27/Llama3.1-8B-Base-Code

Alelcv27/Llama3.1-8B-Base-Math

Merge model

this model