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

Libraries

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

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

pipe = pipeline("text-generation", model="cyboghostginx/Llama3.1-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cyboghostginx/Llama3.1-8B")
model = AutoModelForCausalLM.from_pretrained("cyboghostginx/Llama3.1-8B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use cyboghostginx/Llama3.1-8B with Docker Model Runner:
```
docker model run hf.co/cyboghostginx/Llama3.1-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama3.1-8B

Built with Llama. Unmodified mirror of Meta's Llama 3.1 8B base model. No fine-tuning, no quantization, no change to the weights.


architecture	`LlamaForCausalLM`, 32 layers, vocabulary 128,256
precision	bf16
context	131,072 tokens
formats	safetensors (4 shards) plus `original/consolidated.00.pth` in Meta's checkpoint format

This is the base model, not Instruct. It ships no chat template. Use it for completion or as a fine-tuning starting point; for chat, use meta-llama/Llama-3.1-8B-Instruct.

Apple silicon conversions: 4-bit MLX, 8-bit MLX.

Full model card, benchmarks, training details and responsible-use guidance: meta-llama/Llama-3.1-8B.

License

Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. The full agreement is in LICENSE and reproduced in the gate above. The acceptable use policy is in USE_POLICY.md.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for cyboghostginx/Llama3.1-8B

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1482)

this model

Quantizations

2 models