metadata
license: apache-2.0
language:
- en
- de
- zh
- multilingual
library_name: onnxruntime
tags:
- onnx
- embedding
- text-embedding
- retrieval
- sentence-similarity
- feature-extraction
- fp16
- fastembed
pipeline_tag: sentence-similarity
base_model: codefuse-ai/F2LLM-v2-0.6B
F2LLM-v2-0.6B — FP16 ONNX
FP16-converted ONNX of codefuse-ai/F2LLM-v2-0.6B, a Qwen3-derived 1024-dim retrieval embedding model with 32k context and last-token pooling.
1.2 GB (50 % memory of FP32), retrieval-quality-equivalent to FP32 in our gates.
Quality
| Metric | Value | Threshold |
|---|---|---|
cos_min vs PyTorch FP32 reference (6-text multilingual probe) |
0.999999 | ≥ 0.99 |
cos_mean vs same |
1.000000 | — |
Validated under fastembed-rs' cosine_parity harness on probe/ort-rc12 (ORT 1.24).
Files
| File | Size | Description |
|---|---|---|
model.fp16.onnx |
~5 MB | ONNX header (external data) |
model.fp16.onnx.data |
~1.2 GB | FP16 weights |
tokenizer.json, config.json, tokenizer_config.json, special_tokens_map.json |
small | tokenizer + model config |
Conversion
Streaming FP32→FP16 via convert_fp16_streaming.py (bypasses the 2 GB protobuf serialization limit).
Use via fastembed-rs
let embedder = TextEmbedding::try_new(
InitOptions::new(EmbeddingModel::F2LlmV2_0_6BFp16))?;
let vectors = embedder.embed(vec!["hello world"], None)?;
Pooling: last-token (auto-applied by fastembed-rs). Use the F2LLM instruct format prefix for queries (see the upstream F2LLM repo).
License
Apache 2.0, inherited from the base model.
Provenance and EU AI Act Art. 53 note
- Upstream model: codefuse-ai/F2LLM-v2-0.6B — published by
codefuse-ai. - Upstream licence:
apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (ONNX, F16 precision). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.