Instructions to use groxaxo/octen-embedding-4b-w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/octen-embedding-4b-w4a16 with Transformers:

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

pipe = pipeline("text-generation", model="groxaxo/octen-embedding-4b-w4a16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("groxaxo/octen-embedding-4b-w4a16")
model = AutoModelForCausalLM.from_pretrained("groxaxo/octen-embedding-4b-w4a16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use groxaxo/octen-embedding-4b-w4a16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/octen-embedding-4b-w4a16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/octen-embedding-4b-w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/octen-embedding-4b-w4a16

SGLang

How to use groxaxo/octen-embedding-4b-w4a16 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 "groxaxo/octen-embedding-4b-w4a16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/octen-embedding-4b-w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "groxaxo/octen-embedding-4b-w4a16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/octen-embedding-4b-w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/octen-embedding-4b-w4a16 with Docker Model Runner:
```
docker model run hf.co/groxaxo/octen-embedding-4b-w4a16
```

Octen-Embedding-4B W4A16

This repo contains a W4A16 quantized version of Octen/Octen-Embedding-4B in the validated auto-round-auto-gptq format.

Quantization

Item	Value
Base model	`Octen/Octen-Embedding-4B`
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	3.3 GB
Base size	7.6 GB
Compression	~2.3x
Embedding dim	2560
Layers quantized	252/253; `lm_head` skipped due shape divisibility

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	1.0	1.0	0.0
Recall@5	1.0	1.0	0.0
Mean query cosine, base vs quant	—	0.9783	—
Mean doc cosine, base vs quant	—	0.9777	—

Assessment: retrieval did not degrade on the validation set. The embedding cosine is marginally below a strict 0.98 threshold, but Recall@1/Recall@5 were unchanged. A prior validation run showed query cosine 0.9799 and doc cosine 0.9790.

See validation-4b-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	2.50 GB
Single short-query forward pass	0.6s smoke test; later benchmark ~329ms
Output shape	`[1, 4, 2560]`
Embeddings	Valid normalized vectors; no NaNs observed

Recommended usage

import torch
from transformers import AutoModel, AutoTokenizer

model_id = "groxaxo/octen-embedding-4b-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.

Downloads last month: 395

Safetensors

Model size

0.9B params

Tensor type

F32

I32

F16

Model tree for groxaxo/octen-embedding-4b-w4a16

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-Embedding-4B

Finetuned

Octen/Octen-Embedding-4B

Quantized

(5)

this model