Baltor Get started

Models

w2v-bert-2.0

By facebook. 580 million parameters, a context window of Unknown tokens and the licence mit.

Facts

Released
2023-12-19 the day the repository was first published on Hugging Face Hugging Face, read
Licence
mit the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository Hugging Face, read
Parameters
580 million counted from the safetensors weight files Hugging Face, read
Active parameters
Unknown
Knowledge cutoff
Unknown
Context window
Unknown
Longest output
Unknown
Tool calling
Unknown
Structured output
Unknown
Reasoning controls
Unknown
Inputs and outputs
text in, embeddings out Hugging Face, read
Good for
  • Embeddings: Hugging Face files it under feature-extraction Hugging Face, read

Prices

No source lists a price for this model. It may only run on your own hardware.

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
F16 estimate1.1 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q8_0 estimate0.6 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q4_0 estimate0.3 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers

Start it with a local runtime

  • vLLM
    vllm serve facebook/w2v-bert-2.0
  • SGLang
    sglang serve --model-path facebook/w2v-bert-2.0 --port 30000

Use it from your harness

No endpoint in this directory serves this model yet.

Published results

These are results other people published. Baltor did not run them and does not rank models by them.

Sources of this page