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smithblack-0/
llama3_baseline

Name: llama3_baseline
Availability: InStock

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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

python

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).