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cstr/F2LLM-v2-0.6B-ONNX-FP16

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
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Model Card

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

MetricValueThreshold
cos_min vs PyTorch FP32 reference (6-text multilingual probe)0.999999≥ 0.99
cos_mean vs same1.000000—

Validated under fastembed-rs' cosine_parity harness on probe/ort-rc12 (ORT 1.24).

Files

FileSizeDescription
model.fp16.onnx~5 MBONNX header (external data)
model.fp16.onnx.data~1.2 GBFP16 weights
tokenizer.json, config.json, tokenizer_config.json, special_tokens_map.jsonsmalltokenizer + model config

Conversion

Streaming FP32→FP16 via convert_fp16_streaming.py (bypasses the 2 GB protobuf serialization limit).

Use via fastembed-rs

rust
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.