octen-embedding-8b-w4a16/README.md

license, base_model, library_name, tags

license	base_model	library_name	tags
apache-2.0	Octen/Octen-Embedding-8B	transformers	embeddings quantized w4a16 auto-round auto-gptq

Octen-Embedding-8B W4A16

This repo contains a W4A16 quantized version of Octen/Octen-Embedding-8B in the validated auto-round-auto-gptq format.

Quantization

Item	Value
Base model	Octen/Octen-Embedding-8B
Quantization	W4A16, 4-bit weights / 16-bit activations
Tooling	AutoRound 0.12.2, transformers 5.6.2, torch 2.6.0+cu124
Calibration	8 samples, seqlen 512, 200 iterations, float32 tuning
Quantized size	8.1 GB, 2 shards
Base size	15.0 GB
Compression	~1.9x
Embedding dim	4096
Layers quantized	252/253; lm_head skipped

Validation vs base model

Evaluation used a small retrieval set of 5 query-document pairs, last-token pooling, L2 normalization, and cosine similarity.

Metric	Base	W4A16	Delta
Recall@1	0.8	1.0	+0.2
Recall@5	1.0	1.0	0.0
Mean query cosine, base vs quant	—	0.9840	—
Mean doc cosine, base vs quant	—	0.9820	—

Assessment: this model passed all validation gates cleanly, with >0.98 mean cosine to the base model and no retrieval degradation on the validation set.

See validation-8b-auto-round-auto-gptq.json for the raw metrics.

RTX 3060 smoke test

This quantized model was loaded and run on an RTX 3060 12GB GPU.

Result	Value
VRAM after load	4.53 GB
Single short-query forward pass	0.9s smoke test; later benchmark ~612ms
Output shape	[1, 4, 4096]
Embeddings	Valid normalized vectors; no NaNs observed

Recommended usage

import torch
from transformers import AutoModel, AutoTokenizer

model_id = "groxaxo/octen-embedding-8b-w4a16"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16,
).cuda().eval()

texts = ["how to implement binary search"]
tokens = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")
tokens = {k: v.cuda() for k, v in tokens.items()}

with torch.no_grad():
    out = model(**tokens)

emb = torch.nn.functional.normalize(out.last_hidden_state[:, -1, :], p=2, dim=-1)

Note: the model card records local validation and smoke-test results. For production use, evaluate on your own retrieval distribution.