Instructions to use smithblack-0/llama3_baseline_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smithblack-0/llama3_baseline_dev with Transformers:

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

pipe = pipeline("text-generation", model="smithblack-0/llama3_baseline_dev", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("smithblack-0/llama3_baseline_dev", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use smithblack-0/llama3_baseline_dev with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smithblack-0/llama3_baseline_dev"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smithblack-0/llama3_baseline_dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/smithblack-0/llama3_baseline_dev

SGLang

How to use smithblack-0/llama3_baseline_dev 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 "smithblack-0/llama3_baseline_dev" \
    --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": "smithblack-0/llama3_baseline_dev",
		"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 "smithblack-0/llama3_baseline_dev" \
        --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": "smithblack-0/llama3_baseline_dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use smithblack-0/llama3_baseline_dev with Docker Model Runner:
```
docker model run hf.co/smithblack-0/llama3_baseline_dev
```

llama3_baseline_dev / config.json

smithblack-0

Update architecture and tokenizer

98abb50 verified about 1 month ago

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	{
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration.Llama3Config",
	"AutoModelForCausalLM": "huggingface.Llama3ForCausalLM"
	},
	"head_dim": 48,
	"hidden_size": 768,
	"intermediate_size": 1568,
	"max_position_embeddings": 8192,
	"model_type": "llama3_baseline",
	"num_attention_heads": 16,
	"num_hidden_layers": 24,
	"num_key_value_heads": 4,
	"rms_norm_eps": 1e-05,
	"rope_parameters": null,
	"rope_theta": 500000.0,
	"tie_word_embeddings": false,
	"transformers_version": "5.3.0",
	"use_cache": true,
	"vocab_size": 50277
	}