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jeongseokoh/
Llama-3.1-8B-Instruct_kv_pretrain_both_ce1_attncompmrgw0.1lmidam0.1_compr64_best

Name: Llama-3.1-8B-Instruct_kv_pretrain_both_ce1_attncompmrgw0.1lmidam0.1_compr64_best
Availability: InStock

meta-llama/Llama-3.1-8B-Instruct

finetune

cc-by-4.0

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LatentCOMP checkpoint (Llama-3.1-8B-Instruct_kv_pretrain_both_ce1_attncompmrgw0.1lmidam0.1_compr64, best)

COCOM/PISCO-style compression model for RAG QA (LoRA adapters + memory-token embeddings).

Backbone: meta-llama/Llama-3.1-8B-Instruct
Mode: pretrain
Training form: both
Compression rate: x64 (docs up to 256 tokens -> 4 memory embeddings per doc)

This checkpoint directory stores adapters + small extras; the backbone meta-llama/Llama-3.1-8B-Instruct is loaded from Hugging Face at runtime.

Quickstart (end-to-end)

python

from modelling_pisco import COCOM

# If you pushed this folder to the Hub, use the repo id:
# repo_or_path = "jeongseokoh/Llama-3.1-8B-Instruct_kv_pretrain_both_ce1_attncompmrgw0.1lmidam0.1_compr64_best"
#
# Otherwise, load locally from this checkpoint folder:
repo_or_path = "."

model = COCOM.from_pretrained(repo_or_path).to("cuda")

documents = [[
    "Doc A text ...",
    "Doc B text ..."
]]
questions = ["Question text ..."]

out = model.generate_from_text(
    questions=questions,
    documents=documents,
    max_new_tokens=64,
)
print(out)

Compress once, reuse many times

python

embs = model.compress_documents(documents=documents[0])
out = model.generate_from_compressed_documents_and_questions(
    questions=questions,
    compressed_documents=embs,
    max_new_tokens=64,
)

Tip: crop docs to ~256 tokens before compression for best speed/quality.