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LICENSE ADDED
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+ LFM Open License v1.0
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MODIFICATIONS.md ADDED
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+ # Modifications by RESMP.DEV
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+
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+ This is a derivative of the identified LiquidAI LFM2.5 Encoder checkpoint, not an official LiquidAI release. RESMP.DEV removed the masked-language-model head and contrastively fine-tuned the complete encoder body for code retrieval using the procedure and corpus hashes in `training_report.json`. The resulting weights are stored in BF16.
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+
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+ RESMP.DEV activation-calibrated the BF16 weights with block-GPTQ and packed eligible linear layers as native group-32 MLX MXFP8. Exact settings and hashes are recorded in `quantization_report.json`.
README.md ADDED
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+ ---
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+ license: other
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+ license_name: lfm1.0
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+ license_link: LICENSE
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+ base_model: LiquidAI/LFM2.5-Encoder-350M
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+ pipeline_tag: feature-extraction
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+ library_name: mlx
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+ tags:
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+ - code
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+ - embeddings
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+ - feature-extraction
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+ - mlx
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+ - mxfp8
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+ - gptq
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+ - quantized
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+ ---
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+
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+ # LFM2.5 Encoder 350M Code MXFP8-GPTQ
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+
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+ This is a modified RESMP.DEV research release derived from
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+ [`LiquidAI/LFM2.5-Encoder-350M`](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) at revision `b886781f7c6f10ca9b7096e21b83e30a073c2f39`. It is
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+ not an official Liquid AI release. We removed the masked-language-model head and
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+ contrastively fine-tuned the full bidirectional encoder for multilingual code retrieval.
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+
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+ ## Quantization finding
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+
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+ This is a research artifact, not an automatic recommendation to replace the BF16 model.
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+ Activation calibration is compared with matched native round-to-nearest quantization and
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+ the complete machine-readable receipts are included so mobile and Apple-Silicon users
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+ can evaluate the size, latency, memory, and quality tradeoff themselves.
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+
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+ ## Held-out retrieval results
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+
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+ All rows use the same untouched 6,995-pair multilingual test set, 1,200-character query
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+ and 4,000-character passage caps, query token cap 512, and passage token cap 2,048.
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+ Higher is better. RTN is a matched quantization control; Nomic and Jina are external
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+ service baselines, not architecture-matched controls.
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+
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+ | Model | MRR | R@1 | R@5 | R@10 | NDCG@10 | Python MRR | TypeScript MRR | Artifact |
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+ |---|---:|---:|---:|---:|---:|---:|---:|---:|
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+ | LFM2.5 350M BF16 | 0.3705 | 0.2996 | 0.4422 | 0.5061 | 0.3963 | 0.7969 | 0.1917 | 713.7 MB |
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+ | LFM2.5 350M calibrated MXFP4 | 0.1585 | 0.1169 | 0.1971 | 0.2317 | 0.1697 | 0.5795 | 0.0542 | 291.8 MB |
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+ | LFM2.5 350M RTN MXFP4 | 0.0555 | 0.0422 | 0.0618 | 0.0773 | 0.0576 | 0.3204 | 0.0157 | 291.8 MB |
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+ | LFM2.5 350M calibrated MXFP8 | 0.3710 | 0.3019 | 0.4430 | 0.5045 | 0.3962 | 0.7985 | 0.1903 | 435.5 MB |
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+ | LFM2.5 350M RTN MXFP8 | 0.3684 | 0.2965 | 0.4427 | 0.5054 | 0.3945 | 0.7957 | 0.1932 | 435.4 MB |
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+ | Nomic v1.5 service | 0.5439 | 0.4968 | 0.5954 | 0.6236 | 0.5595 | 0.9289 | 0.3617 | service |
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+ | Jina calibrated MXFP4 | 0.6645 | 0.6133 | 0.7221 | 0.7571 | 0.6832 | 0.9462 | 0.5057 | 1167.7 MB |
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+
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+ A separate BF16 cross-runtime run on `NVIDIA GeForce RTX 3090 Ti` with PyTorch `2.13.0+cu130` produced MRR 0.3709, 616.2 queries/s, 139.1 passages/s, and 1109.0 MB peak CUDA allocation. CUDA throughput is reported separately and is not compared directly with Metal.
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+
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+ A paired 10,000-sample bootstrap estimates calibrated MXFP8 minus BF16 MRR at +0.0005, with a 95% interval of [-0.0010, +0.0021]. A point estimate whose interval crosses zero is not presented as a
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+ quality win.
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+
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+ ## Usage
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+
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+ ```bash
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+ git clone https://github.com/RESMP-DEV/calibrated-code-embeddings
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+ cd calibrated-code-embeddings
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+ uv sync --extra mlx
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+ CODE_EMBEDDING_MODEL_PATH=/path/to/this-model code-embedding-serve --port 1235
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+ ```
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+
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+ The service exposes `POST /v1/embeddings`. It runs the bidirectional LFM2.5 body
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+ directly with MLX; LM Studio is not required. Prefix retrieval queries with `query: `
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+ and candidate code with `passage: ` when calling the model directly.
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+
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+ ## Training and data receipts
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+
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+ Full-backbone symmetric in-batch InfoNCE training used 24,626 language-balanced pairs,
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+ two epochs, batch size 32, learning rate 2e-5, temperature 0.05, and seed 17. The
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+ training report records the NVIDIA RTX A6000 runtime and validation history.
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+
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+ - `train`: 42,626 rows, SHA-256 `426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629`
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+ - `validation`: 5,319 rows, SHA-256 `9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096`
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+ - `test`: 6,995 rows, SHA-256 `9ed10842a12132b6bfb5421df1e2f88dbcfbf6f6e960f36b22eb9ea6e3c72315`
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+ - `calibration`: 4,096 rows, SHA-256 `ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9`
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+
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+ The corpus combines pinned CodeSearchNet data with pinned permissively licensed code
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+ repositories. Exact and token 8-gram near-duplicates were removed with test-before-
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+ validation-before-train precedence. See `corpus_receipt.json`, `source_receipt.json`,
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+ `training_report.json`, `quantization_report.json` when present, `benchmarks/`, and
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+ `artifact_manifest.json` for machine-readable evidence.
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+
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+ ## License and attribution
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+
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+ The weights retain the LFM Open License v1.0 in `LICENSE`, including its attribution and
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+ commercial-use conditions. `MODIFICATIONS.md` identifies RESMP.DEV's changes. The
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+ [training and quantization workbench](https://github.com/RESMP-DEV/calibrated-code-embeddings)
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+ is separately MIT licensed.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{liquidAI2026Encoders,
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+ author = {Liquid AI},
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+ title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
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+ journal = {Liquid AI Blog},
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+ year = {2026},
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+ note = {www.liquid.ai/blog/lfm2-5-encoders},
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+ }
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+ ```
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+ """LFM2 backbone with bidirectional attention + non-causal short-conv.
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+
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+ Wired into the HF repo via `auto_map` in config.json so that
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+
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+ None) and runs the kernel non-causally via `Lfm2Attention.is_causal = False`,
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+ yielding outputs equivalent to the unpadded forward.
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+ """
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+
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+ from typing import Optional
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+
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+ import torch
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+ import torch.nn as nn
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+ import torch.nn.functional as F
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
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+ from transformers.modeling_utils import PreTrainedModel
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+ from transformers.models.lfm2 import modeling_lfm2 as _lfm2_mod
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+ from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
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+ from transformers.models.lfm2.modeling_lfm2 import (
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+ Lfm2Attention,
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+ Lfm2Model,
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+ Lfm2PreTrainedModel,
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+ Lfm2ShortConv,
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+ apply_mask_to_padding_states,
34
+ )
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+
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+
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+ def _bidirectional_mask(
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+ config,
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+ cache_position: Optional[torch.LongTensor] = None,
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+ past_key_values=None,
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+ ) -> Optional[torch.Tensor]:
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+ # (input_embeds <-> inputs_embeds); accept either to stay forward-compatible.
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+ if input_embeds is None:
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+ input_embeds = kwargs.get("inputs_embeds")
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+ if config._attn_implementation == "flash_attention_2":
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+ # controlled by `Lfm2Attention.is_causal` (set to False below).
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+ if attention_mask is not None and not attention_mask.all():
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+ mask = torch.zeros((bsz, 1, q_len, kv_len), device=device, dtype=dtype)
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+ if attention_mask is not None:
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+ cur_len = attention_mask.size(-1)
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+ key_pad_flags = (attention_mask == 0).to(device=device, dtype=torch.float32)
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+ pad_vec = torch.zeros((bsz, kv_len), device=device, dtype=torch.float32)
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+ if cur_len > 0:
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+ return mask
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+
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+
75
+ def _noncausal_shortconv_forward(
76
+ self,
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+ past_key_values=None,
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+ cache_position=None,
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+ attention_mask: Optional[torch.Tensor] = None,
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+ **kwargs,
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+ ) -> torch.Tensor:
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+ x = apply_mask_to_padding_states(hidden_states, attention_mask)
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+
85
+ BCx = self.in_proj(x).transpose(-1, -2)
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+ B, C, x = BCx.chunk(3, dim=-2)
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91
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+ stride=1, padding=pad, dilation=1, groups=Bx.shape[1],
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+ )
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+ if conv_out.shape[-1] > Bx.shape[-1]:
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+ conv_out = conv_out[..., :Bx.shape[-1]]
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+ elif conv_out.shape[-1] < Bx.shape[-1]:
98
+ conv_out = F.pad(conv_out, (0, Bx.shape[-1] - conv_out.shape[-1]))
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+ y = C * conv_out
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+ y = y.transpose(-1, -2).contiguous()
102
+ return self.out_proj(y)
103
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+
105
+ def _shortconv_forward(self, *args, **kwargs):
106
+ return self.slow_forward(*args, **kwargs)
107
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+ _PATCHED = False
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+
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+
112
+ def _install_patches() -> None:
113
+ global _PATCHED
114
+ if _PATCHED:
115
+ return
116
+ _lfm2_mod.create_causal_mask = _bidirectional_mask
117
+ Lfm2ShortConv.slow_forward = _noncausal_shortconv_forward
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+ Lfm2ShortConv.forward = _shortconv_forward
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+ _PATCHED = True
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+
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+
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+ _install_patches()
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+
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+
125
+ def _set_attention_noncausal(model) -> None:
126
+ for module in model.modules():
127
+ if isinstance(module, Lfm2Attention):
128
+ module.is_causal = False
129
+
130
+
131
+ class Lfm2BidirectionalModel(Lfm2Model):
132
+ """LFM2 patched for encoder-style use:
133
+ full bidirectional attention + non-causal short-conv."""
134
+
135
+ def __init__(self, config):
136
+ _install_patches()
137
+ super().__init__(config)
138
+ _set_attention_noncausal(self)
139
+
140
+
141
+ class Lfm2BidirectionalForMaskedLM(Lfm2PreTrainedModel):
142
+ """LFM2 bidirectional encoder with a tied masked-LM head."""
143
+
144
+ config_class = Lfm2Config
145
+ base_model_prefix = "lfm2"
146
+ _tied_weights_keys = {"lm_head.weight": "lfm2.embed_tokens.weight"}
147
+
148
+ def __init__(self, config: Lfm2Config):
149
+ _install_patches()
150
+ config = type(config).from_dict({**config.to_dict(), "use_cache": False})
151
+ super().__init__(config)
152
+ self.lfm2 = Lfm2BidirectionalModel(config)
153
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
154
+ self.post_init()
155
+ self.lm_head.weight = self.lfm2.embed_tokens.weight
156
+
157
+ def get_input_embeddings(self):
158
+ return self.lfm2.embed_tokens
159
+
160
+ def set_input_embeddings(self, value):
161
+ self.lfm2.embed_tokens = value
162
+
163
+ def get_output_embeddings(self):
164
+ return self.lm_head
165
+
166
+ def set_output_embeddings(self, new_embeddings):
167
+ self.lm_head = new_embeddings
168
+
169
+ def forward(
170
+ self,
171
+ input_ids: Optional[torch.LongTensor] = None,
172
+ attention_mask: Optional[torch.Tensor] = None,
173
+ position_ids: Optional[torch.LongTensor] = None,
174
+ inputs_embeds: Optional[torch.FloatTensor] = None,
175
+ labels: Optional[torch.LongTensor] = None,
176
+ output_hidden_states: Optional[bool] = None,
177
+ output_attentions: Optional[bool] = None,
178
+ return_dict: Optional[bool] = None,
179
+ **kwargs,
180
+ ) -> MaskedLMOutput:
181
+ return_dict = True if return_dict is None else return_dict
182
+ outputs = self.lfm2(
183
+ input_ids=input_ids,
184
+ attention_mask=attention_mask,
185
+ position_ids=position_ids,
186
+ inputs_embeds=inputs_embeds,
187
+ use_cache=False,
188
+ output_attentions=output_attentions,
189
+ output_hidden_states=output_hidden_states,
190
+ return_dict=True,
191
+ )
192
+ hidden = outputs.last_hidden_state
193
+ logits = self.lm_head(hidden)
194
+
195
+ loss = None
196
+ if labels is not None:
197
+ loss = F.cross_entropy(
198
+ logits.view(-1, self.config.vocab_size),
199
+ labels.view(-1),
200
+ ignore_index=-100,
201
+ )
202
+
203
+ if not return_dict:
204
+ out = (logits,) + outputs[1:]
205
+ return ((loss,) + out) if loss is not None else out
206
+ return MaskedLMOutput(
207
+ loss=loss,
208
+ logits=logits,
209
+ hidden_states=outputs.hidden_states,
210
+ attentions=outputs.attentions,
211
+ )
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+ "path": "1_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]
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