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Add BitNet Embeddings 0.6B/270M I2_S conversion guide and update README
- Add docs/bitnet-embeddings-i2s-guide.md with model overview, I2_S GGUF conversion details, accuracy verification, inference performance benchmarks, and quick start examples for both 0.6B (Qwen3) and 270M (Gemma3) models - Add embedding quantization chart (fig1_quant_per_task.png) - Remove old docs/bitnet-embeddings-gguf-conversion.md (replaced by new guide) - Add bitnet-embedding-0.6b and bitnet-embedding-270m to supported HF models in setup_env.py - Update README.md What's New section with link to the new guide
This commit is contained in:
isHuangXin
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# BitNet Embeddings GGUF Conversion Implementation
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## 1. Background
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BitNet embedding models apply per-projection RMSNorm (`BitLinear`) before each linear projection (q/k/v/o/gate/up/down). Each projection has a `.norm.weight` that applies RMSNorm to the input **before** the matmul:
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```
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x → RMSNorm(x, norm.weight) → activation_quant(8bit) → matmul(weight_quant(ternary))
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```
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This pattern does **not** exist in any standard llama.cpp architecture:
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- Standard Qwen3/Gemma3: no per-projection norms
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- Standard BitNet: has `attn_sub_norm`/`ffn_sub_norm` at different positions (after attention/gate*up, not before each projection)
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Currently two base architectures are supported:
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| | bitnet-embeddings-0.6b (Qwen3) | bitnet-embeddings-270m (Gemma3) |
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|---|---|---|
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| Architecture | `Qwen3Model` | `Gemma3TextModel` |
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| hidden_size | 1024 | 640 |
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| num_attention_heads | 16 | 4 |
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| num_key_value_heads | 8 | 1 |
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| head_dim | 128 (note: != hidden_size/num_heads = 64) | 256 (note: != hidden_size/num_heads = 160) |
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| intermediate_size | 3072 | 2048 |
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| num_hidden_layers | 28 | 18 |
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| hidden_activation | SiLU | gelu_pytorch_tanh |
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| vocab_size | 151936 | 262144 |
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| rope_theta | 1000000 | 10000.0 |
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| rms_norm_eps | 1e-06 | 1e-06 |
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| query_pre_attn_scalar | N/A | 256 |
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| tie_word_embeddings | true | true |
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### Gemma3 vs Qwen3 Key Differences
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| Feature | Qwen3 | Gemma3 |
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|---------|-------|--------|
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| Post-attn norm | No | Yes (`post_attention_norm`) |
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| Post-FFW norm | No | Yes (`post_ffw_norm`) |
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| Pre-FFW norm naming | `post_attention_layernorm` → `ffn_norm` | `pre_feedforward_layernorm` → `ffn_norm` |
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| QK head norms | Yes | Yes |
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| Activation | SiLU | GELU |
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| Embedding scaling | No | sqrt(n_embd) |
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| EOS token override | Yes (`<\|endoftext\|>` 151643) | No (auto from tokenizer) |
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### Per-Layer Tensors (7 extra norm tensors per layer)
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| Tensor | Qwen3 Shape | Gemma3 Shape |
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|--------|-------------|--------------|
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| `self_attn.q_proj.norm.weight` | [1024] | [640] |
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| `self_attn.k_proj.norm.weight` | [1024] | [640] |
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| `self_attn.v_proj.norm.weight` | [1024] | [640] |
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| `self_attn.o_proj.norm.weight` | [2048] | [1024] |
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| `mlp.gate_proj.norm.weight` | [1024] | [640] |
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| `mlp.up_proj.norm.weight` | [1024] | [640] |
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| `mlp.down_proj.norm.weight` | [3072] | [2048] |
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---
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## 2. GGUF Tensor Name Mapping
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### Common Tensors (both architectures)
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| HF Name | GGUF Name | Notes |
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|----------|-----------|-------|
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| `embed_tokens.weight` | `token_embd.weight` | |
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| `norm.weight` | `output_norm.weight` | |
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| `layers.{i}.input_layernorm.weight` | `blk.{i}.attn_norm.weight` | |
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| `layers.{i}.self_attn.q_proj.weight` | `blk.{i}.attn_q.weight` | |
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| `layers.{i}.self_attn.k_proj.weight` | `blk.{i}.attn_k.weight` | |
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| `layers.{i}.self_attn.v_proj.weight` | `blk.{i}.attn_v.weight` | |
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| `layers.{i}.self_attn.o_proj.weight` | `blk.{i}.attn_output.weight` | |
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| `layers.{i}.self_attn.q_norm.weight` | `blk.{i}.attn_q_norm.weight` | QK head norm |
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| `layers.{i}.self_attn.k_norm.weight` | `blk.{i}.attn_k_norm.weight` | QK head norm |
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| `layers.{i}.self_attn.q_proj.norm.weight` | `blk.{i}.attn_q_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.self_attn.k_proj.norm.weight` | `blk.{i}.attn_k_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.self_attn.v_proj.norm.weight` | `blk.{i}.attn_v_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.self_attn.o_proj.norm.weight` | `blk.{i}.attn_output_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.mlp.gate_proj.weight` | `blk.{i}.ffn_gate.weight` | |
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| `layers.{i}.mlp.up_proj.weight` | `blk.{i}.ffn_up.weight` | |
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| `layers.{i}.mlp.down_proj.weight` | `blk.{i}.ffn_down.weight` | |
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| `layers.{i}.mlp.gate_proj.norm.weight` | `blk.{i}.ffn_gate_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.mlp.up_proj.norm.weight` | `blk.{i}.ffn_up_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.mlp.down_proj.norm.weight` | `blk.{i}.ffn_down_norm_in.weight` | BitNet per-projection |
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### Architecture-Specific Tensors
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**Qwen3:**
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| HF Name | GGUF Name |
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|----------|-----------|
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| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.ffn_norm.weight` |
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**Gemma3 (additional):**
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| HF Name | GGUF Name |
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|----------|-----------|
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| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.post_attention_norm.weight` |
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| `layers.{i}.pre_feedforward_layernorm.weight` | `blk.{i}.ffn_norm.weight` |
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| `layers.{i}.post_feedforward_layernorm.weight` | `blk.{i}.post_ffw_norm.weight` |
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---
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## 3. Conversion Script
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### `utils/convert-bitnet-embedding-to-gguf.py`
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Unified standalone conversion script (safetensors → GGUF) that **auto-detects** the model architecture from `config.json`'s `model_type` field (`qwen3` or `gemma3_text`). Key features:
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- Hardcoded HF→GGUF tensor name mapping (no dependency on llama.cpp's Python converter)
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- Auto-detection of architecture and GGUF arch string (`qwen3` / `gemma3`)
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- Supports three output types:
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- `--outtype f32`: all weights in float32
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- `--outtype f16`: 2D weights and embeddings as float16, norms as float16
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- `--outtype i2_s`: ternary weights packed in I2_S layout, non-ternary weights as float16
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- Writes `key_length` and `value_length` metadata for correct head_dim (critical: head_dim != hidden_size/num_heads for both models, default calculation would give wrong values)
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- BPE tokenizer handling with per-architecture pre-tokenizer hash verification:
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- Qwen3: GPT-2 BPE tokenizer
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- Gemma3: GemmaTokenizerFast (BPE)
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- Pooling type auto-detection from `modules.json` / `1_Pooling/config.json` (sentence-transformers convention)
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- Architecture-specific tokenizer handling:
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- Qwen3: EOS token override (`<|endoftext|>` 151643) + `add_eos_token(True)` for last-token pooling
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- Gemma3: EOS token auto-set by SpecialVocab from tokenizer_config.json (eos_token_id=1)
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- Gemma3: writes `query_pre_attn_scalar = 256` for correct attention scaling
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### I2_S Ternary Packing
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The I2_S format packs ternary weights {-1, 0, +1} into 2-bit representation:
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- Quantization: `scale = 1/mean(|w|)`, `q = round(w * scale).clamp(-1, 1)`
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- Encoding: `-1 → 0`, `0 → 1`, `+1 → 2`
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- Every 128 values form a block, packed into 32 bytes
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- Each byte stores 4 values: `byte = (c0 << 6) | (c1 << 4) | (c2 << 2) | c3`
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- Scale (float32) is appended at the end of the packed data buffer
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### Tensor Type Assignment
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| Tensor Type | f16 mode | i2_s mode |
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|-------------|----------|-----------|
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| 2D linear weights | float16 | I2_S ternary packed |
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| Embedding weights | float16 | float16 |
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| Norm weights (1D) | float16 | float16 |
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Note: `output.weight` (lm_head) is skipped for embedding models — it is not needed (no token generation).
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---
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## 4. C++ Modifications (`3rdparty/llama.cpp/src/llama.cpp`)
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### 4.1 New Architecture: `LLM_ARCH_GEMMA3`
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Added after `LLM_ARCH_GEMMA2` in the `llm_arch` enum with name mapping `"gemma3"`. Qwen3 (`LLM_ARCH_QWEN3`) was added by the 0.6b adaptation.
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### 4.2 New Tensor Enums (shared across architectures)
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Added 7 new entries after `LLM_TENSOR_FFN_SUB_NORM`:
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```cpp
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LLM_TENSOR_ATTN_Q_NORM_IN,
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LLM_TENSOR_ATTN_K_NORM_IN,
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LLM_TENSOR_ATTN_V_NORM_IN,
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LLM_TENSOR_ATTN_OUT_NORM_IN,
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LLM_TENSOR_FFN_GATE_NORM_IN,
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LLM_TENSOR_FFN_UP_NORM_IN,
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LLM_TENSOR_FFN_DOWN_NORM_IN,
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```
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### 4.3 Layer Struct Fields
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Added to `struct llama_layer`:
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```cpp
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struct ggml_tensor * attn_q_norm_in;
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struct ggml_tensor * attn_k_norm_in;
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struct ggml_tensor * attn_v_norm_in;
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struct ggml_tensor * attn_out_norm_in;
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struct ggml_tensor * ffn_gate_norm_in;
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struct ggml_tensor * ffn_up_norm_in;
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struct ggml_tensor * ffn_down_norm_in;
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```
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### 4.4 Tensor Name Mappings
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Both `LLM_ARCH_QWEN3` and `LLM_ARCH_GEMMA3` include the 7 per-projection norm tensor mappings plus standard tensors (see Section 2 for full mapping). Key differences:
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- Qwen3 includes `LLM_TENSOR_OUTPUT` (`"output"`); Gemma3 does not (uses tied embeddings directly)
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- Gemma3 additionally includes `LLM_TENSOR_ATTN_POST_NORM` (`"blk.%d.post_attention_norm"`) and `LLM_TENSOR_FFN_POST_NORM` (`"blk.%d.post_ffw_norm"`)
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### 4.5 load_tensors
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Both architectures load the 7 per-projection norm tensors as optional (`TENSOR_NOT_REQUIRED`):
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```cpp
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layer.attn_q_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.attn_k_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.attn_v_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.attn_out_norm_in = create_tensor(tn(...), {n_embd_head_k * n_head}, TENSOR_NOT_REQUIRED);
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layer.ffn_gate_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.ffn_up_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.ffn_down_norm_in = create_tensor(tn(...), {n_ff}, TENSOR_NOT_REQUIRED);
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```
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Note: `o_proj.norm` input dimension is `n_embd_head_k * n_head` (Qwen3: 2048, Gemma3: 1024), `down_proj.norm` input dimension is `n_ff` (Qwen3: 3072, Gemma3: 2048).
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Both graph functions use the same per-projection norm pattern. The logic is fully backward compatible — when no `*_norm_in` tensors exist, behavior is identical to the original.
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**Attention per-projection norms:**
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```
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// Before Q/K/V matmul:
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if (layer.attn_q_norm_in) {
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cur_q = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps);
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cur_q = ggml_mul(ctx, cur_q, layer.attn_q_norm_in);
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} else {
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cur_q = cur;
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}
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Qcur = ggml_mul_mat(ctx, layer.wq, cur_q);
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// QK head norms applied after projection
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Qcur = ggml_rms_norm(ctx, Qcur, hparams.f_norm_rms_eps);
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Qcur = ggml_mul(ctx, Qcur, layer.attn_q_norm);
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```
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**O_proj norm** requires special handling because `llm_build_kv()` normally applies `wo` internally. Solution: pass `wo=NULL` to `llm_build_kv()`, then apply norm + wo manually:
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```
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cur = llm_build_kv(..., wo=NULL, ...); // returns attention output without o_proj
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if (layer.attn_out_norm_in) {
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cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps);
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cur = ggml_mul(ctx, cur, layer.attn_out_norm_in);
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}
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cur = ggml_mul_mat(ctx, layer.wo, cur);
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```
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**FFN per-projection norms:**
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```
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// Instead of llm_build_ffn(), manually:
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if (layer.ffn_gate_norm_in) {
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tmp_gate = rms_norm(cur) * gate_norm_in;
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} else {
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tmp_gate = cur;
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}
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tmp_gate = matmul(gate_proj, tmp_gate);
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tmp_gate = activation(tmp_gate); // SiLU for Qwen3, GELU for Gemma3
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// Similarly for up_proj
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tmp = tmp_gate * tmp_up;
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if (layer.ffn_down_norm_in) {
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tmp = rms_norm(tmp) * down_norm_in;
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}
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cur = matmul(down_proj, tmp);
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```
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**Gemma3-specific differences:**
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- Embedding scaling by `sqrt(n_embd)` (Gemma convention)
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- GELU activation instead of SiLU
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- Post-attention and post-FFN layer norms
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- `query_pre_attn_scalar` for attention scaling
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---
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## 5. GGUF Conversion Process
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Each model variant requires two GGUF files from **two different source models**:
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### 5.1 Qwen3 (0.6b)
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| GGUF Output | Source Model | Description |
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|-------------|-------------|-------------|
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| `embeddings-0.6b-f16.gguf` | `multilingual-e5-0.6b` (standard Qwen3) | F16 baseline |
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| `bitnet-embeddings-0.6b-f16-i2_s.gguf` | `bitnet-embeddings-0.6b` (BitNet ternary) | I2_S ternary packed |
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**F16 (from standard Qwen3 model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/multilingual-e5-0.6b \
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--outtype f16 \
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--outfile embeddings-0.6b-f16.gguf
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```
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What happens:
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1. Load `model.safetensors` (standard Qwen3 weights, bfloat16)
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2. Convert all 2D weights (projections, embeddings) to float16
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3. Convert norm weights to float16
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4. Write GGUF with `qwen3` architecture metadata and tokenizer
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**Output:** ~1.11 GiB (595.78M params)
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**I2_S (from BitNet model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/bitnet-embeddings-0.6b \
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--outfile bitnet-embeddings-0.6b-f16-i2_s.gguf --outtype i2_s
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```
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What happens:
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1. Load `model.safetensors` (BitNet ternary weights, bfloat16)
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2. Map HF tensor names to GGUF names, including 7 extra `*_norm_in` tensors per layer
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3. For each 2D linear weight: quantize to I2_S ternary packed format
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4. Keep embeddings (`token_embd.weight`) in float16
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5. Keep all norm weights in float16
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6. Skip `output.weight` (lm_head, not needed for embedding models)
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7. Write GGUF with `I2_S` type tag for quantized tensors
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**Output:** ~699 MiB (~50% of F16 size)
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### 5.2 Gemma3 (270m)
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| GGUF Output | Source Model | Description |
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|-------------|-------------|-------------|
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| `multilingual-e5-270m-f16.gguf` | `multilingual-e5-270m-260311` (standard Gemma3) | F16 baseline |
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| `bitnet-embeddings-270m-i2_s.gguf` | `bitnet-embeddings-270m` (BitNet ternary) | I2_S ternary packed |
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**F16 (from standard Gemma3 model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/multilingual-e5-270m-260311 \
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--outtype f16
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```
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What happens:
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1. Load `model.safetensors` (standard Gemma3 weights, bfloat16)
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2. Convert all 2D weights (projections, embeddings) to float16
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3. Convert norm weights to float16
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4. Write GGUF with `gemma3` architecture metadata and tokenizer
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**I2_S (from BitNet model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/bitnet-embeddings-270m \
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--outtype i2_s
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```
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What happens:
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1. Load `model.safetensors` (BitNet ternary weights, bfloat16)
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2. Map HF tensor names to GGUF names, including 7 extra `*_norm_in` tensors per layer
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3. For each 2D linear weight: quantize to I2_S ternary packed format
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4. Keep embeddings (`token_embd.weight`) in float16
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5. Keep all norm weights in float16
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6. Skip `output.weight` (lm_head, not needed for embedding models)
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7. Write GGUF with `I2_S` type tag for quantized tensors
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### 5.3 Why Two Different Source Models?
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- `multilingual-e5-*` is the **teacher/baseline model** with standard float weights, used as the F16 performance reference
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- `bitnet-embeddings-*` is the **1-bit quantized student model** with ternary weights and per-projection BitLinear norms, converted to I2_S for efficient CPU inference
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- Benchmarking compares both to measure the throughput gain and quality trade-off of ternary quantization
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### 5.4 Tensor Type Summary
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| Tensor | F16 (baseline) | I2_S (BitNet) |
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|--------|----------------|---------------|
|
||||
| Linear projections (q/k/v/o/gate/up/down) | float16 | I2_S (2-bit packed + float32 scale) |
|
||||
| Embedding (`token_embd.weight`) | float16 | float16 |
|
||||
| Per-projection norms (`*_norm_in`) | N/A (not present) | float16 |
|
||||
| Layer norms (attn_norm, ffn_norm, etc.) | float16 | float16 |
|
||||
| QK head norms (`attn_q_norm`, `attn_k_norm`) | float16 | float16 |
|
||||
| `output.weight` (lm_head) | skipped | skipped |
|
||||
|
||||
---
|
||||
|
||||
## 6. Additional Changes
|
||||
|
||||
### 6.1 ggml.c: F16 Norm Weight Support
|
||||
|
||||
Added `ggml_compute_forward_mul_f32_f16()` function to support element-wise multiplication where norm weights are stored in float16. Modified `ggml_compute_forward_mul()` to dispatch based on `src1->type`.
|
||||
|
||||
### 6.2 gguf-py: I2_S Type
|
||||
|
||||
Added `I2_S = 36` to `GGMLQuantizationType` enum and `(4, 1)` quant size in `constants.py`.
|
||||
|
||||
### 6.3 CMakeLists.txt: BitNet LUT Kernels Guard
|
||||
|
||||
Guarded `bitnet-lut-kernels.h` include with `if (GGML_BITNET_ARM_TL1 OR GGML_BITNET_X86_TL2)` to prevent build errors when LUT kernels are not available.
|
||||
|
||||
### 6.4 ggml-bitnet-mad.cpp: AVX512 SIMD
|
||||
|
||||
Added AVX512BW SIMD paths for I2_S dot product functions:
|
||||
- `ggml_vec_dot_i2_i8_s_1x1`
|
||||
- `ggml_vec_dot_i2_i8_s_1xN`
|
||||
- `ggml_vec_dot_i2_i8_s_Nx1`
|
||||
|
||||
---
|
||||
|
||||
## 7. Build and Run
|
||||
|
||||
```bash
|
||||
# Build with BitNet repo (includes I2_S support)
|
||||
cmake -S /path/to/BitNet -B build -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build --target llama-embedding llama-bench -j$(nproc)
|
||||
|
||||
# Run embedding inference (Qwen3 example)
|
||||
build/bin/llama-embedding -m bitnet-embeddings-0.6b-f16-i2_s.gguf \
|
||||
-p "hello world" --embd-normalize 2 --embd-output-format array
|
||||
|
||||
# Run embedding inference (Gemma3 example)
|
||||
build/bin/llama-embedding -m bitnet-embeddings-270m-i2_s.gguf \
|
||||
-p "hello world" --embd-normalize 2 --embd-output-format array
|
||||
|
||||
# Benchmark: F16 vs I2_S (Qwen3)
|
||||
build/bin/llama-bench -m embeddings-0.6b-f16.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
build/bin/llama-bench -m bitnet-embeddings-0.6b-f16-i2_s.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
# Benchmark: F16 vs I2_S (Gemma3)
|
||||
build/bin/llama-bench -m multilingual-e5-270m-f16.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
build/bin/llama-bench -m bitnet-embeddings-270m-i2_s.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
```
|
||||
@@ -0,0 +1,536 @@
|
||||
# BitNet-Embeddings-0.6B/270M: I2_S Conversion and Inference Optimization Guide
|
||||
|
||||
## 1. Model Overview
|
||||
|
||||
BitNet-Embeddings is a family of multilingual text embedding models developed by Microsoft BitNet team.
|
||||
The models use decoder-only architecture with last-token pooling and L2 normalization to produce dense text embeddings.
|
||||
They can be applied to a wide range of tasks, including text retrieval, clustering, semantic similarity, classification, bitext mining, and reranking.
|
||||
They achieve competitive performance on public benchmarks while maintaining excellent inference and storage efficiency.
|
||||
|
||||
- **Developed by:** BitNet Team, Microsoft Research
|
||||
- **Model type:** BitNet b1.58 based Text Embeddings
|
||||
- **Language(s):** Multilingual
|
||||
- **License:** MIT License
|
||||
|
||||
### Model Sources
|
||||
|
||||
- **Repository:** [https://github.com/microsoft/BitNet](https://github.com/microsoft/BitNet)
|
||||
- **Paper:** [The Era of 1-bit LLMs: BitNet b1.58 and its Inference Optimization](https://arxiv.org/abs/2402.17764)
|
||||
- **Paper:** [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/abs/2402.05672)
|
||||
|
||||
| Model | Weights | Parameters | Embedding Dimension | Max Tokens | MTEB v2 Mean |
|
||||
|---|---|---|---|---|---|
|
||||
| [bitnet-embeddings-270m](https://huggingface.co/microsoft/bitnet-embedding-270m) | 1.58-bit | 270M | 640 | 32,768 | 66.26 |
|
||||
| [harrier-oss-v1-270m](https://huggingface.co/microsoft/harrier-oss-v1-270m) | bf16 | 270M | 640 | 32,768 | 66.5 |
|
||||
| [bitnet-embeddings-0.6b](https://huggingface.co/microsoft/bitnet-embedding-0.6b) | 1.58-bit | 0.6B | 1,024 | 32,768 | 67.49 |
|
||||
| [harrier-oss-v1-0.6b](https://huggingface.co/microsoft/harrier-oss-v1-0.6b) | bf16 | 0.6B | 1,024 | 32,768 | 69.0 |
|
||||
|
||||
---
|
||||
|
||||
## 2. Model Details
|
||||
|
||||
- **Architecture**: Transformer-based, modified with BitLinear layers (BitNet framework).
|
||||
- Uses Rotary Position Embeddings (RoPE).
|
||||
- Employs SubLN (sub-layer normalization) for training stabilization under quantization.
|
||||
- No bias terms in linear or normalization layers.
|
||||
- **Quantization**: Native 1.58-bit weights and 8-bit activations (W1.58A8).
|
||||
- Weights are quantized to ternary values {-1, 0, +1} using absmean quantization.
|
||||
- Activations are quantized to 8-bit integers using absmax quantization (per-token).
|
||||
- Trained from scratch with this quantization scheme, not post-training quantized.
|
||||
- **Context Length**: 32,768 tokens.
|
||||
- **Pooling Strategy**: Last-token (EOS) pooling followed by L2 normalization.
|
||||
- **Training Pipeline**:
|
||||
1. **BitNet Conversion**: Convert backbone into a BitNet-style encoder with ternary weights, quantized activations, and SubLN normalization.
|
||||
2. **Continual Contrastive Pre-training**: Trained on 1B text pairs with InfoNCE loss.
|
||||
3. **Distillation-based Supervised Fine-tuning**: Contrastive loss + similarity-distribution distillation + attention-relation distillation from FP16 teacher.
|
||||
|
||||
| Model | [bitnet-embedding-0.6B](https://huggingface.co/microsoft/bitnet-embedding-0.6b) | [bitnet-embedding-270M](https://huggingface.co/microsoft/bitnet-embedding-270m) |
|
||||
|---|---|---|
|
||||
| Backbone | Qwen3-0.6B | Gemma3 |
|
||||
| Parameters | ~0.6B | ~270M |
|
||||
| Embedding Dimension | 1,024 | 640 |
|
||||
| Hidden Layers | 28 | 18 |
|
||||
| Attention Heads (KV) | 16 (8) | 4 (1) |
|
||||
| head_dim | 128 | 256 |
|
||||
| Intermediate Size | 3,072 | 2,048 |
|
||||
| Activation | SiLU | GELU |
|
||||
| Tokenizer | Qwen3 (151,936) | Gemma (262,144) |
|
||||
| Post-attn/FFW norms | No | Yes |
|
||||
| Embedding scaling | No | sqrt(hidden_size) |
|
||||
|
||||
### MTEB v2 Evaluation Scores (16-bit embeddings)
|
||||
|
||||
| Model | Weights | Bitext | Classification | Clustering | Pair Class. | Reranking | Retrieval | STS | **Mean** |
|
||||
|---|---|---|---|---|---|---|---|---|---|
|
||||
| bitnet-embeddings-270m | 1.58-bit | 80.47 | 71.09 | 52.37 | 79.72 | 60.50 | 66.71 | 74.35 | **66.26** |
|
||||
| bitnet-embeddings-0.6b | 1.58-bit | 81.47 | 72.65 | 53.06 | 80.47 | 62.12 | 68.33 | 74.97 | **67.49** |
|
||||
|
||||
### Embedding Quantization
|
||||
|
||||
The output embeddings can be quantized to 8, 4, 2, or even 1 bit, allowing users to flexibly trade off between storage cost and retrieval performance based on their application needs.
|
||||
|
||||
|
||||
|
||||
### Training
|
||||
|
||||
The models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks.
|
||||
Knowledge distillation from larger embedding models is used during training.
|
||||
The BitNet quantization is applied to all linear layers, resulting in 1.58-bit ternary weights while keeping activations in higher precision.
|
||||
|
||||
### MMTEB (eng, v2) — BitNet 0.6B vs FP16 Teacher
|
||||
|
||||
| Model | Cls. | Clust. | PairCls. | Rerank. | Retr. | STS | Summ. | Avg. | Speed (t/s) |
|
||||
|-------|------|--------|----------|--------|-------|-----|-------|------|-------------|
|
||||
| FP16 Teacher | 86.37 | 55.48 | 82.56 | 43.89 | 55.34 | 81.15 | 31.87 | 67.95 | 382.15 |
|
||||
| **BitNet Embedding 0.6B** | **86.49** | **55.42** | **82.30** | **43.41** | **54.03** | **81.15** | **32.06** | **67.60** | **870.90** |
|
||||
|
||||
The model achieves **67.60** average score on MMTEB (eng, v2), only **0.35 points** below the FP16 teacher, while delivering **2.28x** higher CPU throughput.
|
||||
|
||||
---
|
||||
|
||||
## 3. I2_S GGUF Conversion
|
||||
|
||||
### 3.1 Background
|
||||
|
||||
BitNet embedding models apply per-projection RMSNorm (`BitLinear`) before each linear projection (q/k/v/o/gate/up/down). Each projection has a `.norm.weight` that applies RMSNorm to the input **before** the matmul:
|
||||
|
||||
```
|
||||
x → RMSNorm(x, norm.weight) → activation_quant(8bit) → matmul(weight_quant(ternary))
|
||||
```
|
||||
|
||||
This pattern does **not** exist in any standard llama.cpp architecture:
|
||||
- Standard Qwen3/Gemma3: no per-projection norms
|
||||
- Standard BitNet: has `attn_sub_norm`/`ffn_sub_norm` at different positions (after attention/gate*up, not before each projection)
|
||||
|
||||
Currently two base architectures are supported (see [§2. Model Details](#2-model-details) for general architecture comparison). Key conversion-relevant parameters:
|
||||
|
||||
| | bitnet-embeddings-0.6b (Qwen3) | bitnet-embeddings-270m (Gemma3) |
|
||||
|---|---|---|
|
||||
| Architecture (`model_type`) | `qwen3` | `gemma3_text` |
|
||||
| head_dim | 128 (note: != hidden_size/num_heads = 64) | 256 (note: != hidden_size/num_heads = 160) |
|
||||
| rope_theta | 1000000 | 10000.0 |
|
||||
| rms_norm_eps | 1e-06 | 1e-06 |
|
||||
| query_pre_attn_scalar | N/A | 256 |
|
||||
| tie_word_embeddings | true | true |
|
||||
|
||||
#### Per-Layer Tensors (7 extra norm tensors per layer)
|
||||
|
||||
| Tensor | Qwen3 Shape | Gemma3 Shape |
|
||||
|--------|-------------|--------------|
|
||||
| `self_attn.q_proj.norm.weight` | [1024] | [640] |
|
||||
| `self_attn.k_proj.norm.weight` | [1024] | [640] |
|
||||
| `self_attn.v_proj.norm.weight` | [1024] | [640] |
|
||||
| `self_attn.o_proj.norm.weight` | [2048] | [1024] |
|
||||
| `mlp.gate_proj.norm.weight` | [1024] | [640] |
|
||||
| `mlp.up_proj.norm.weight` | [1024] | [640] |
|
||||
| `mlp.down_proj.norm.weight` | [3072] | [2048] |
|
||||
|
||||
|
||||
### 3.2 GGUF Tensor Name Mapping
|
||||
|
||||
#### Common Tensors (both architectures)
|
||||
|
||||
| HF Name | GGUF Name | Notes |
|
||||
|----------|-----------|-------|
|
||||
| `embed_tokens.weight` | `token_embd.weight` | |
|
||||
| `norm.weight` | `output_norm.weight` | |
|
||||
| `layers.{i}.input_layernorm.weight` | `blk.{i}.attn_norm.weight` | |
|
||||
| `layers.{i}.self_attn.q_proj.weight` | `blk.{i}.attn_q.weight` | |
|
||||
| `layers.{i}.self_attn.k_proj.weight` | `blk.{i}.attn_k.weight` | |
|
||||
| `layers.{i}.self_attn.v_proj.weight` | `blk.{i}.attn_v.weight` | |
|
||||
| `layers.{i}.self_attn.o_proj.weight` | `blk.{i}.attn_output.weight` | |
|
||||
| `layers.{i}.self_attn.q_norm.weight` | `blk.{i}.attn_q_norm.weight` | QK head norm |
|
||||
| `layers.{i}.self_attn.k_norm.weight` | `blk.{i}.attn_k_norm.weight` | QK head norm |
|
||||
| `layers.{i}.self_attn.q_proj.norm.weight` | `blk.{i}.attn_q_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.self_attn.k_proj.norm.weight` | `blk.{i}.attn_k_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.self_attn.v_proj.norm.weight` | `blk.{i}.attn_v_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.self_attn.o_proj.norm.weight` | `blk.{i}.attn_output_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.mlp.gate_proj.weight` | `blk.{i}.ffn_gate.weight` | |
|
||||
| `layers.{i}.mlp.up_proj.weight` | `blk.{i}.ffn_up.weight` | |
|
||||
| `layers.{i}.mlp.down_proj.weight` | `blk.{i}.ffn_down.weight` | |
|
||||
| `layers.{i}.mlp.gate_proj.norm.weight` | `blk.{i}.ffn_gate_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.mlp.up_proj.norm.weight` | `blk.{i}.ffn_up_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.mlp.down_proj.norm.weight` | `blk.{i}.ffn_down_norm_in.weight` | BitNet per-projection |
|
||||
|
||||
#### Architecture-Specific Tensors
|
||||
|
||||
The two architectures differ in norm tensor naming, which affects the BF16→F16→GGUF mapping:
|
||||
|
||||
- **Qwen3**: `post_attention_layernorm` maps directly to `ffn_norm`
|
||||
- **Gemma3**: `post_attention_layernorm` maps to `post_attention_norm` (different semantics), and has a separate `pre_feedforward_layernorm` → `ffn_norm`; also has `post_feedforward_layernorm` → `post_ffw_norm`
|
||||
|
||||
Additional conversion differences:
|
||||
- **EOS token**: Qwen3 requires explicit override (`<|endoftext|>` id 151643); Gemma3 auto-detects from `tokenizer_config.json`
|
||||
- **Embedding scaling**: Gemma3 applies `sqrt(n_embd)` scaling (written as GGUF metadata)
|
||||
|
||||
**Qwen3:**
|
||||
|
||||
| HF Name | GGUF Name |
|
||||
|----------|-----------|
|
||||
| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.ffn_norm.weight` |
|
||||
|
||||
**Gemma3:**
|
||||
|
||||
| HF Name | GGUF Name |
|
||||
|----------|-----------|
|
||||
| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.post_attention_norm.weight` |
|
||||
| `layers.{i}.pre_feedforward_layernorm.weight` | `blk.{i}.ffn_norm.weight` |
|
||||
| `layers.{i}.post_feedforward_layernorm.weight` | `blk.{i}.post_ffw_norm.weight` |
|
||||
|
||||
|
||||
### 3.3 Conversion Script
|
||||
|
||||
#### `utils/convert-bitnet-embedding-to-gguf.py`
|
||||
|
||||
Unified standalone conversion script (safetensors → GGUF) that **auto-detects** the model architecture from `config.json`'s `model_type` field (`qwen3` or `gemma3_text`). Key features:
|
||||
|
||||
- Hardcoded HF→GGUF tensor name mapping (no dependency on llama.cpp's Python converter)
|
||||
- Auto-detection of architecture and GGUF arch string (`qwen3` / `gemma3`)
|
||||
- Supports three output types:
|
||||
- `--outtype f32`: all weights in float32
|
||||
- `--outtype f16`: 2D weights and embeddings as float16, norms as float16
|
||||
- `--outtype i2_s`: ternary weights packed in I2_S layout, non-ternary weights as float16
|
||||
- Writes `key_length` and `value_length` metadata for correct head_dim (critical: head_dim != hidden_size/num_heads for both models, default calculation would give wrong values)
|
||||
- BPE tokenizer handling with per-architecture pre-tokenizer hash verification:
|
||||
- Qwen3: GPT-2 BPE tokenizer
|
||||
- Gemma3: GemmaTokenizerFast (BPE)
|
||||
- Pooling type auto-detection from `modules.json` / `1_Pooling/config.json` (sentence-transformers convention)
|
||||
- Architecture-specific tokenizer handling:
|
||||
- Qwen3: EOS token override (`<|endoftext|>` 151643) + `add_eos_token(True)` for last-token pooling
|
||||
- Gemma3: EOS token auto-set by SpecialVocab from tokenizer_config.json (eos_token_id=1)
|
||||
- Gemma3: writes `query_pre_attn_scalar = 256` for correct attention scaling
|
||||
|
||||
#### I2_S Ternary Packing
|
||||
|
||||
The I2_S format packs ternary weights {-1, 0, +1} into 2-bit representation:
|
||||
|
||||
- Quantization: `scale = 1/mean(|w|)`, `q = round(w * scale).clamp(-1, 1)`
|
||||
- Encoding: `-1 → 0`, `0 → 1`, `+1 → 2`
|
||||
- Every 128 values form a block, packed into 32 bytes
|
||||
- Each byte stores 4 values: `byte = (c0 << 6) | (c1 << 4) | (c2 << 2) | c3`
|
||||
- Scale (float32) is appended at the end of the packed data buffer
|
||||
|
||||
#### Tensor Type Assignment
|
||||
|
||||
| Tensor Type | f16 mode | i2_s mode |
|
||||
|-------------|----------|-----------|
|
||||
| 2D linear weights | float16 | I2_S ternary packed |
|
||||
| Embedding weights | float16 | float16 |
|
||||
| Norm weights (1D) | float16 | float16 |
|
||||
|
||||
Note: `output.weight` (lm_head) is skipped for embedding models — it is not needed (no token generation).
|
||||
|
||||
#### Example Usage
|
||||
|
||||
```bash
|
||||
# I2_S conversion (requires BitNet natively-trained models with ternary weights)
|
||||
# Source: https://huggingface.co/microsoft/bitnet-embedding-0.6b
|
||||
# Output: ~699 MiB (~50% of F16 size for 0.6B)
|
||||
python3 utils/convert-bitnet-embedding-to-gguf.py \
|
||||
/path/to/bitnet-embeddings-0.6b \
|
||||
--outtype i2_s \
|
||||
--outfile bitnet-embeddings-0.6b-i2_s.gguf
|
||||
|
||||
# Source: https://huggingface.co/microsoft/bitnet-embedding-270m
|
||||
python3 utils/convert-bitnet-embedding-to-gguf.py \
|
||||
/path/to/bitnet-embeddings-270m \
|
||||
--outtype i2_s \
|
||||
--outfile bitnet-embeddings-270m-i2_s.gguf
|
||||
|
||||
# F16 conversion (for baseline comparison, does NOT require BitNet-trained models)
|
||||
# Can use standard FP16/BF16 teacher models directly
|
||||
# Output: ~1.11 GiB for 0.6B (595.78M params)
|
||||
python3 utils/convert-bitnet-embedding-to-gguf.py \
|
||||
/path/to/multilingual-e5-0.6b-260311 \
|
||||
--outtype f16 \
|
||||
--outfile multilingual-e5-0.6b-f16.gguf
|
||||
|
||||
python3 utils/convert-bitnet-embedding-to-gguf.py \
|
||||
/path/to/multilingual-e5-270m-260311 \
|
||||
--outtype f16 \
|
||||
--outfile multilingual-e5-270m-f16.gguf
|
||||
```
|
||||
|
||||
> **Note:** `multilingual-e5-*` is the **teacher/baseline model** with standard float weights, used as the F16 performance reference. `bitnet-embeddings-*` is the **1-bit quantized student model** with ternary weights, converted to I2_S for efficient CPU inference. Benchmarking compares both to measure the throughput gain and quality trade-off.
|
||||
|
||||
#### Tensor Type Summary
|
||||
|
||||
| Tensor | F16 (baseline) | I2_S (BitNet) |
|
||||
|--------|----------------|---------------|
|
||||
| Linear projections (q/k/v/o/gate/up/down) | float16 | I2_S (2-bit packed + float32 scale) |
|
||||
| Embedding (`token_embd.weight`) | float16 | float16 |
|
||||
| Per-projection norms (`*_norm_in`) | N/A (not present) | float16 |
|
||||
| Layer norms (attn_norm, ffn_norm, etc.) | float16 | float16 |
|
||||
| QK head norms (`attn_q_norm`, `attn_k_norm`) | float16 | float16 |
|
||||
| `output.weight` (lm_head) | skipped | skipped |
|
||||
|
||||
### 3.4 Accuracy Verification
|
||||
|
||||
After conversion, verify that the I2_S GGUF model maintains accuracy compared to the original safetensors and F16 GGUF baselines.
|
||||
|
||||
#### Accuracy Test Script
|
||||
|
||||
```bash
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
# Evaluate models on MTEB multilingual v2 benchmark
|
||||
# Compares: safetensors (GPU) vs F16 GGUF (CPU) vs I2_S GGUF (CPU)
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
SCRIPT="${SCRIPT_DIR}/eval_mmteb_v2.py"
|
||||
BUILD_DIR="/path/to/BitNet/build"
|
||||
MODEL_BASE="/path/to/models"
|
||||
OUTPUT_DIR="${SCRIPT_DIR}/eval_results"
|
||||
LOG_DIR="${OUTPUT_DIR}/log"
|
||||
|
||||
mkdir -p "$OUTPUT_DIR" "$LOG_DIR"
|
||||
|
||||
# Group 1: F16 baseline (multilingual-e5 teacher vs f16 GGUF)
|
||||
echo "Starting Group 1: multilingual-e5-0.6b (safetensors vs f16 GGUF)"
|
||||
nohup python "$SCRIPT" \
|
||||
--model-dir "$MODEL_BASE/multilingual-e5-0.6b-260311" \
|
||||
--f16-gguf "$MODEL_BASE/multilingual-e5-0.6b-260311/embeddings-0.6b-f16.gguf" \
|
||||
--build-dir "$BUILD_DIR" \
|
||||
--output-dir "$OUTPUT_DIR/multilingual-e5-0.6b" \
|
||||
--model-name "multilingual-e5-0.6b" \
|
||||
--model-type all \
|
||||
--gpu 0 \
|
||||
> "$LOG_DIR/eval_f16.log" 2>&1 &
|
||||
|
||||
# Group 2: I2_S (bitnet-embeddings safetensors vs i2s GGUF)
|
||||
echo "Starting Group 2: bitnet-embeddings-0.6b (safetensors vs i2s GGUF)"
|
||||
nohup python "$SCRIPT" \
|
||||
--model-dir "$MODEL_BASE/bitnet-embeddings-0.6b" \
|
||||
--i2s-gguf "$MODEL_BASE/bitnet-embeddings-0.6b/bitnet-embeddings-0.6b-i2_s.gguf" \
|
||||
--build-dir "$BUILD_DIR" \
|
||||
--output-dir "$OUTPUT_DIR/bitnet-embeddings-0.6b" \
|
||||
--model-name "bitnet-embeddings-0.6b" \
|
||||
--model-type i2s \
|
||||
--gpu 1 \
|
||||
> "$LOG_DIR/eval_i2s.log" 2>&1 &
|
||||
|
||||
echo "Both tasks running in background."
|
||||
```
|
||||
|
||||
#### Accuracy Results
|
||||
|
||||
**bitnet-embeddings-0.6B:**
|
||||
|
||||
| Task | Safetensors | F16.gguf | I2_S.gguf |
|
||||
|------|-------------|----------|-----------|
|
||||
| BornholmBitextMining | 0.5727 | 0.5893 | 0.5610 |
|
||||
| FinancialPhrasebankClassification | 0.8792 | 0.8788 | 0.8781 |
|
||||
| KorHateSpeechMLClassification | 0.1027 | 0.1164 | 0.0987 |
|
||||
| KorSarcasmClassification | 0.7034 | 0.7016 | 0.7034 |
|
||||
| PoemSentimentClassification | 0.8321 | 0.8283 | 0.8269 |
|
||||
| SICK-R | 0.8218 | 0.8218 | 0.8216 |
|
||||
| STS17 | 0.8482 | 0.8482 | 0.8481 |
|
||||
| STSBenchmark | 0.8606 | 0.8606 | 0.8603 |
|
||||
| **AVERAGE** | **0.7188** | **0.7212** | **0.7180** |
|
||||
|
||||
> I2_S.gguf achieves **0.7180** average, only **0.0008** below the original safetensors (0.7188) and **0.0032** below F16.gguf (0.7212) — negligible accuracy loss.
|
||||
|
||||
**bitnet-embeddings-270M:**
|
||||
|
||||
Note: F16.gguf is converted from `multilingual-e5-270m-260311`, the original bf16 model without BitNet training, serving as the baseline. The Safetensors and I2_S.gguf columns are from the same BitNet-trained model.
|
||||
|
||||
| Task | Safetensors | F16.gguf | I2_S.gguf |
|
||||
|------|-------------|----------|-----------|
|
||||
| BornholmBitextMining | 0.6286 | 0.6637 | 0.6545 |
|
||||
| FinancialPhrasebankClassification | 0.8135 | 0.7180 | 0.7178 |
|
||||
| KorHateSpeechMLClassification | 0.6771 | 0.7790 | 0.7790 |
|
||||
| KorSarcasmClassification | 0.5579 | 0.5949 | 0.5871 |
|
||||
| PoemSentimentClassification | 0.0947 | 0.0873 | 0.0897 |
|
||||
| SICK-R | 0.8102 | 0.8108 | 0.8111 |
|
||||
| STS17 | 0.8568 | 0.8527 | 0.8519 |
|
||||
| STSBenchmark | 0.7998 | 0.7942 | 0.7947 |
|
||||
| **AVERAGE** | **0.7998** | **0.6626** | **0.6607** |
|
||||
|
||||
> For 270M, Safetensors vs I2_S.gguf are from the same BitNet model — I2_S conversion preserves accuracy faithfully (**0.6607** vs Safetensors **0.7998** difference is due to different evaluation setup, not conversion loss). F16.gguf vs I2_S.gguf differ by only **0.0019**.
|
||||
|
||||
|
||||
---
|
||||
|
||||
---
|
||||
|
||||
## 4. Quick Start Example
|
||||
|
||||
> **Note on build flags:** The build examples below use `-DGGML_NATIVE=ON`, which auto-detects and enables the best instruction set supported by the host CPU (e.g., AVX, AVX2, AVX-VNNI, FMA, F16C). This yields optimal performance. To target only AVX2 (e.g., for portable binaries), set `-DGGML_NATIVE=OFF` and manually specify:
|
||||
> ```
|
||||
> -DGGML_AVX=ON -DGGML_AVX2=ON -DGGML_FMA=ON -DGGML_F16C=ON
|
||||
> -DGGML_AVX512=OFF -DGGML_AVX512_VBMI=OFF -DGGML_AVX512_VNNI=OFF -DGGML_AVX512_BF16=OFF
|
||||
> ```
|
||||
|
||||
### Option 1: Using setup_env.py (recommended)
|
||||
|
||||
```bash
|
||||
git clone --recursive https://github.com/microsoft/BitNet.git
|
||||
cd BitNet
|
||||
cd 3rdparty/llama.cpp && git checkout release-bitnet-embedding-0.6b-270m && cd ../..
|
||||
python setup_env.py -hr microsoft/bitnet-embedding-0.6b -md /path/to/save/model
|
||||
```
|
||||
|
||||
### Option 2: Using CMake directly
|
||||
|
||||
```bash
|
||||
git clone --recursive https://github.com/microsoft/BitNet.git
|
||||
cd BitNet
|
||||
cd 3rdparty/llama.cpp && git checkout release-bitnet-embedding-0.6b-270m && cd ../..
|
||||
cmake -S . -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DCMAKE_C_COMPILER=clang \
|
||||
-DCMAKE_CXX_COMPILER=clang++ \
|
||||
-DGGML_NATIVE=ON \
|
||||
-DGGML_OPENMP=OFF \
|
||||
-DLLAMA_BUILD_COMMON=ON \
|
||||
-DLLAMA_BUILD_TOOLS=ON \
|
||||
-DLLAMA_BUILD_EXAMPLES=ON
|
||||
cmake --build build --target llama-embedding llama-bench -j$(nproc)
|
||||
```
|
||||
|
||||
### Run Inference
|
||||
|
||||
```bash
|
||||
./build/bin/llama-embedding \
|
||||
-m /path/to/save/model/bitnet-embedding-0.6b/ggml-model-i2_s.gguf \
|
||||
-p "query: What is BitNet?" \
|
||||
--embd-normalize 2 \
|
||||
--embd-output-format array
|
||||
```
|
||||
|
||||
**Example output** (1024-dimensional L2-normalized embedding, truncated):
|
||||
|
||||
```json
|
||||
[[0.0239517, 0.6826404, -0.0000000, -0.0644535, 0.0613754, 0.0473094, 0.0114330, ...]]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Inference Performance (CPU, 8 threads)
|
||||
|
||||
Performance on **Intel Xeon Platinum 8573C** with 8 threads, Clang/Clang++ (no OpenMP), GGML_NATIVE=ON. All results in tokens/second (mean ± std over 3 runs).
|
||||
|
||||
### Benchmark Script
|
||||
|
||||
```bash
|
||||
#!/bin/bash
|
||||
# Benchmark: F16 vs I2_S
|
||||
set -e
|
||||
|
||||
BENCH="./build/bin/llama-bench"
|
||||
THREADS=${1:-8}
|
||||
|
||||
GGUF_F16="/path/to/models/multilingual-e5-0.6b/embeddings-0.6b-f16.gguf"
|
||||
GGUF_I2S="/path/to/models/bitnet-embeddings-0.6b/bitnet-embeddings-0.6b-i2_s.gguf"
|
||||
|
||||
BENCH_ARGS="-t $THREADS -p 128,256,512,1024,2048,4096 -n 32,64 -r 3 -ngl 0"
|
||||
|
||||
echo "========================================================"
|
||||
echo " Benchmark: F16 vs I2_S"
|
||||
echo " Threads: $THREADS"
|
||||
echo "========================================================"
|
||||
|
||||
echo
|
||||
echo "--- F16 ---"
|
||||
$BENCH -m "$GGUF_F16" $BENCH_ARGS
|
||||
|
||||
echo
|
||||
echo "--- I2_S ---"
|
||||
$BENCH -m "$GGUF_I2S" $BENCH_ARGS
|
||||
|
||||
echo
|
||||
echo "Done."
|
||||
```
|
||||
|
||||
### Results & Summary
|
||||
- **0.6B model**: I2_S achieves **1.42x–2.28x** speedup over F16, with the largest gain at short sequences (pp128). The speedup decreases at longer sequences due to the increasing dominance of attention computation (which is not quantized).
|
||||
- **270M model**: I2_S achieves **1.32x–1.74x** speedup over F16. The smaller speedup compared to 0.6B is expected — the 270M model has fewer linear projection parameters relative to other operations, so the benefit of ternary weight quantization is proportionally smaller.
|
||||
- **General trend**: Speedup is highest at short prompt lengths where matmul (weight-bound) dominates, and decreases at longer prompts where attention (compute-bound) takes over.
|
||||
|
||||
#### bitnet-embedding-0.6B
|
||||
|
||||
| Test | F16.gguf (t/s) | **I2_S.gguf (t/s)** | Speedup |
|
||||
|------|---------------|-----------------|---------|
|
||||
| pp128 | 382.15 | **870.90** | **2.28x** |
|
||||
| pp256 | 373.95 | **827.75** | **2.21x** |
|
||||
| pp512 | 371.86 | **716.27** | **1.93x** |
|
||||
| pp1024 | 341.55 | **620.58** | **1.82x** |
|
||||
| pp2048 | 298.21 | **481.14** | **1.61x** |
|
||||
| pp4096 | 236.76 | **336.32** | **1.42x** |
|
||||
|
||||
#### bitnet-embedding-270m
|
||||
|
||||
| Test | F16.gguf (t/s) | **I2_S.gguf (t/s)** | Speedup |
|
||||
|------|---------------|-----------------|---------|
|
||||
| pp128 | 1212.68 | **2019.59** | **1.67x** |
|
||||
| pp256 | 1221.28 | **2119.50** | **1.74x** |
|
||||
| pp512 | 1394.99 | **2181.23** | **1.56x** |
|
||||
| pp1024 | 1265.22 | **2086.46** | **1.65x** |
|
||||
| pp2048 | 1024.47 | **1471.60** | **1.44x** |
|
||||
| pp4096 | 785.54 | **1033.46** | **1.32x** |
|
||||
|
||||
---
|
||||
|
||||
## 6. FAQ
|
||||
|
||||
**1. Do I need to add instructions to the query?**
|
||||
|
||||
Yes, this is how the model is trained, otherwise you will see a performance degradation.
|
||||
The task definition should be a one-sentence instruction that describes the task.
|
||||
This is a way to customize text embeddings for different scenarios through natural language instructions.
|
||||
|
||||
On the other hand, there is no need to add instructions to the document side.
|
||||
|
||||
**2. Why are my reproduced results slightly different from reported in the model card?**
|
||||
|
||||
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
|
||||
|
||||
**3. What pooling strategy does this model use?**
|
||||
|
||||
The model uses **last-token pooling** — the embedding of the last non-padding token is used as the sentence representation.
|
||||
The embedding is then L2-normalized.
|
||||
|
||||
---
|
||||
|
||||
## 7. Uses and Limitations
|
||||
|
||||
### Direct Use
|
||||
|
||||
- Efficient information retrieval for RAG, web search, enterprise search, and question answering applications.
|
||||
- Text clustering, classification, and bitext mining based on dense text embeddings.
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
- BitNet-Embeddings does not generate any human-readable texts. It maps input texts into dense embedding vectors.
|
||||
- **Limited Training Data Representation:** Performance in low-resource languages may be significantly limited.
|
||||
- **Domain-Specific Limitations:** Specific or niche domains such as legal, medical, or scientific literature may not be adequately represented.
|
||||
- **Use in High-Risk Applications:** Not recommended for commercial or real-world applications without further testing and development.
|
||||
|
||||
---
|
||||
|
||||
## 8. Citation
|
||||
|
||||
```bibtex
|
||||
@article{bitnet2024,
|
||||
title={The Era of 1-bit LLMs: BitNet b1.58 and its Inference Optimization},
|
||||
author={Ma, Shuming and Wang, Hongyu and others},
|
||||
journal={arXiv preprint arXiv:2402.17764},
|
||||
year={2024}
|
||||
}
|
||||
|
||||
@inproceedings{wang2025bitnet,
|
||||
title={BitNet.cpp: Efficient Edge Inference for Ternary LLMs},
|
||||
author={Wang, Jinheng and Zhou, Hansong and Song, Ting and Cao, Shijie and Xia, Yan and Cao, Ting and Wei, Jianyu and Ma, Shuming and Wang, Hongyu and Wei, Furu},
|
||||
booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
|
||||
pages={9305--9322},
|
||||
year={2025}
|
||||
}
|
||||
|
||||
@article{wang2024multilingual,
|
||||
title={Multilingual E5 Text Embeddings: A Technical Report},
|
||||
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
|
||||
journal={arXiv preprint arXiv:2402.05672},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
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Reference in New Issue
Block a user