Instructions to use smithblack-0/llama3_baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smithblack-0/llama3_baseline with Transformers:

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

pipe = pipeline("text-generation", model="smithblack-0/llama3_baseline", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("smithblack-0/llama3_baseline", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use smithblack-0/llama3_baseline 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/smithblack-0/llama3_baseline

SGLang

How to use smithblack-0/llama3_baseline 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use smithblack-0/llama3_baseline with Docker Model Runner:
```
docker model run hf.co/smithblack-0/llama3_baseline
```

advanced-transformers-lib -- Llama 3 Baseline

A Llama 3-style decoder-only transformer architecture for research. No pretrained weights -- pull the architecture from the Hub and instantiate a freshly initialised model from config. Override any parameter at instantiation time.

Important: trust_remote_code=True is required. It downloads the architecture source files from the Hub and imports them into your Python process. Review the source at smithblack-0/llama3_baseline before use.

Usage

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

# Pull architecture config -- override any parameter at instantiation time
config = AutoConfig.from_pretrained(
    "smithblack-0/llama3_baseline",
    trust_remote_code=True,
    num_hidden_layers=16,  # example override
)

# Instantiate with fresh random weights -- no checkpoint required
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("smithblack-0/llama3_baseline")

# Save and reload after training
model.save_pretrained("./checkpoint")
model = AutoModelForCausalLM.from_pretrained("./checkpoint", trust_remote_code=True)

Default Configuration

Parameter	Default
`vocab_size`	50277
`hidden_size`	768
`intermediate_size`	1568
`num_hidden_layers`	24
`num_attention_heads`	16
`num_key_value_heads`	4
`head_dim`	48
`max_position_embeddings`	8192
`rope_theta`	500000.0

License

MIT. Clean-room synthesis: the human author has not read the Llama source code. Architectural decisions derive from the published paper. Tokenizer is GPT-NeoX (EleutherAI/gpt-neox-20b, Apache 2.0).

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