Instructions to use lemon07r/Lllama-3-RedElixir-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemon07r/Lllama-3-RedElixir-8B with Transformers:

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

pipe = pipeline("text-generation", model="lemon07r/Lllama-3-RedElixir-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lemon07r/Lllama-3-RedElixir-8B")
model = AutoModelForCausalLM.from_pretrained("lemon07r/Lllama-3-RedElixir-8B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lemon07r/Lllama-3-RedElixir-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemon07r/Lllama-3-RedElixir-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemon07r/Lllama-3-RedElixir-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lemon07r/Lllama-3-RedElixir-8B

SGLang

How to use lemon07r/Lllama-3-RedElixir-8B 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 "lemon07r/Lllama-3-RedElixir-8B" \
    --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": "lemon07r/Lllama-3-RedElixir-8B",
		"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 "lemon07r/Lllama-3-RedElixir-8B" \
        --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": "lemon07r/Lllama-3-RedElixir-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lemon07r/Lllama-3-RedElixir-8B with Docker Model Runner:
```
docker model run hf.co/lemon07r/Lllama-3-RedElixir-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lllama-3-RedElixir-8B

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

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using NousResearch/Meta-Llama-3-8B 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: NousResearch/Meta-Llama-3-8B
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
slices:
- sources:
  - layer_range: [0, 32]
    model: NousResearch/Meta-Llama-3-8B
  - layer_range: [0, 32]
    model: nbeerbower/llama-3-spicy-abliterated-stella-8B
    parameters:
      density: 0.6
      weight: 0.22
  - layer_range: [0, 32]
    model: flammenai/Mahou-1.2-llama3-8B
    parameters:
      density: 0.6
      weight: 0.22
  - layer_range: [0, 32]
    model: hf-100/Llama-3-Spellbound-Instruct-8B-0.3
    parameters:
      density: 0.58
      weight: 0.14
  - layer_range: [0, 32]
    model: zeroblu3/NeuralPoppy-EVO-L3-8B
    parameters:
      density: 0.58
      weight: 0.14
  - layer_range: [0, 32]
    model: Nitral-AI/Hathor_Stable-v0.2-L3-8B
    parameters:
      density: 0.56
      weight: 0.1
  - layer_range: [0, 32]
    model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot
    parameters:
      density: 0.56
      weight: 0.1
  - layer_range: [0, 32]
    model: emnakamura/llama-3-MagicDolphin-8B
    parameters:
      density: 0.55
      weight: 0.08