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Llama 2

The second Llama generation, ported to pure Keras 3. Same decoder shape as Llama (RMSNorm, SwiGLU, rotary embeddings) with grouped-query attention on the larger variants and a 4K context window. The -chat checkpoints are the RLHF-tuned conversational models.

Links:

See also llama.md, llama4.md.

Variants

Load any of these with from_weights("<variant>").

Variant Hub
llama2-7b meta-llama/Llama-2-7b-hf
llama2-7b-chat meta-llama/Llama-2-7b-chat-hf
llama2-13b meta-llama/Llama-2-13b-hf
llama2-13b-chat meta-llama/Llama-2-13b-chat-hf
llama2-70b meta-llama/Llama-2-70b-hf
llama2-70b-chat meta-llama/Llama-2-70b-chat-hf
codellama-7b codellama/CodeLlama-7b-hf
codellama-13b codellama/CodeLlama-13b-hf
codellama-34b codellama/CodeLlama-34b-hf
codellama-70b codellama/CodeLlama-70b-hf

API

Llama2Model

The decoder backbone, no LM head. Returns {"last_hidden_state": (batch, seq, embed_dim)}.

Arg Default Meaning
vocab_size 32000 token vocabulary size
embed_dim 4096 model width
mlp_dim 11008 MLP inner width
num_layers 32 decoder blocks
num_heads 32 query heads
num_kv_heads 32 key/value heads (GQA)
head_dim None per-head width
norm_eps 1e-05 RMSNorm epsilon
rope_theta 10000.0 rotary base frequency
tie_embeddings False reuse the embedding matrix as the LM head

Llama2Generate

Llama2Model plus a (tied) LM head. Returns {"logits": (batch, seq, vocab_size)} and adds .generate(). Same constructor arguments as Llama2Model.

generate(input_ids, attention_mask=None, max_new_tokens=None,
         eos_token_id=None, sampler=None, seed=None, **prefill_inputs)
Arg Default Meaning
input_ids required (batch, seq) token ids
attention_mask None (batch, seq) 1 = keep, 0 = padding
max_new_tokens None tokens to generate
eos_token_id None stop token (defaults to the tokenizer's)
sampler None sampling strategy; greedy when unset
seed None seed for stochastic samplers

Llama2Tokenizer

Tokenizer on the tokenizers backend.

Llama2Tokenizer(hf_id=None, tokenizer_file=None)
Arg Default Meaning
hf_id None Hub repo to pull the tokenizer files from
tokenizer_file None explicit path to a tokenizer.json

Calling it returns {"input_ids", "attention_mask"}, padded across the batch. It accepts a plain string, a list of strings (a batch), or a chat-message list, which is routed through apply_chat_template automatically. Decode with .decode(ids) for one sequence or .batch_decode(ids) for a batch.

End-to-end example

Single input

import os
os.environ["KERAS_BACKEND"] = "torch"   # or "jax" / "tensorflow"

from kerasformers.models.llama2 import Llama2Generate, Llama2Tokenizer

model = Llama2Generate.from_weights("llama2-7b")
tokenizer = Llama2Tokenizer.from_weights("llama2-7b")

inputs = tokenizer([{"role": "user", "content": "Explain rotary embeddings in one sentence."}])
outputs = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(outputs[0]))

Batch

Pass a list of strings. The tokenizer pads them and generate runs the batch together:

prompts = [
    "The capital of France is",
    "In one sentence, what is a transformer?",
    "Write a haiku about GPUs.",
]
inputs = tokenizer(prompts)              # {"input_ids": (3, seq), "attention_mask": (3, seq)}
outputs = model.generate(**inputs, max_new_tokens=64)

for text in tokenizer.batch_decode(outputs):
    print(text)

Backbone only

from kerasformers.models.llama2 import Llama2Model

backbone = Llama2Model.from_weights("llama2-7b")
hidden = backbone(inputs)["last_hidden_state"]   # (batch, seq, embed_dim)

Loading from the Hub

Any Hub repo with this architecture works via the hf: prefix, including community fine-tunes:

model = Llama2Generate.from_weights("hf:meta-llama/Llama-2-7b-hf")

Lower memory

Larger checkpoints load in bf16 or weight-only quantized. See quantization.md:

model = Llama2Generate.from_weights(
    "llama2-7b", quantization="int8", low_memory=True, load_dtype="bfloat16"
)