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

Libraries

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

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Llama3.1-8B-Base-TIES-Math-Code / README.md

Alelcv27

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metadata

base_model:
  - Alelcv27/Llama3.1-8B-Base-Math
  - meta-llama/Llama-3.1-8B
  - Alelcv27/Llama3.1-8B-Base-Code
library_name: transformers
tags:
  - mergekit
  - merge

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

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

Merge Details

Merge Method

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

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: meta-llama/Llama-3.1-8B
dtype: float16
merge_method: ties
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 32]
        model: Alelcv27/Llama3.1-8B-Base-Math
        parameters:
          density: 0.8
          weight: 0.8
      - layer_range: [0, 32]
        model: Alelcv27/Llama3.1-8B-Base-Code
        parameters:
          density: 0.8
          weight: 0.7
      - layer_range: [0, 32]
        model: meta-llama/Llama-3.1-8B
parameters:
  normalize: 1.0