Add files using upload-large-folder tool

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

LICENSE

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+LFM Open License v1.0 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+END OF TERMS AND CONDITIONS

MODIFICATIONS.md

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+# Modifications by RESMP.DEV
+
+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.
+
+RESMP.DEV activation-calibrated the BF16 weights with block-GPTQ and packed eligible linear layers as native group-32 MLX MXFP4. Exact settings and hashes are recorded in `quantization_report.json`.

README.md

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+---
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+base_model: LiquidAI/LFM2.5-Encoder-230M
+pipeline_tag: feature-extraction
+library_name: mlx
+tags:
+- code
+- embeddings
+- feature-extraction
+- mlx
+- mxfp4
+- gptq
+- quantized
+---
+
+# LFM2.5 Encoder 230M Code MXFP4-GPTQ
+
+This is a modified RESMP.DEV research release derived from
+[`LiquidAI/LFM2.5-Encoder-230M`](https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M) at revision `0b649ad0c684378b03d4d8304f7577a662ab89bc`. It is
+not an official Liquid AI release. We removed the masked-language-model head and
+contrastively fine-tuned the full bidirectional encoder for multilingual code retrieval.
+
+## Quantization finding
+
+This is a research artifact, not an automatic recommendation to replace the BF16 model.
+Activation calibration is compared with matched native round-to-nearest quantization and
+the complete machine-readable receipts are included so mobile and Apple-Silicon users
+can evaluate the size, latency, memory, and quality tradeoff themselves.
+
+## Held-out retrieval results
+
+All rows use the same untouched 6,995-pair multilingual test set, 1,200-character query
+and 4,000-character passage caps, query token cap 512, and passage token cap 2,048.
+Higher is better. RTN is a matched quantization control; Nomic and Jina are external
+service baselines, not architecture-matched controls.
+
+| Model | MRR | R@1 | R@5 | R@10 | NDCG@10 | Python MRR | TypeScript MRR | Artifact |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| LFM2.5 230M BF16 | 0.3960 | 0.3212 | 0.4756 | 0.5375 | 0.4235 | 0.8360 | 0.2527 | 464.2 MB |
+| LFM2.5 230M calibrated MXFP4 | 0.2110 | 0.1620 | 0.2572 | 0.3029 | 0.2262 | 0.7113 | 0.0693 | 225.5 MB |
+| LFM2.5 230M RTN MXFP4 | 0.0806 | 0.0609 | 0.0931 | 0.1139 | 0.0844 | 0.4435 | 0.0191 | 225.4 MB |
+| LFM2.5 230M calibrated MXFP8 | 0.3934 | 0.3197 | 0.4715 | 0.5342 | 0.4207 | 0.8394 | 0.2447 | 306.8 MB |
+| LFM2.5 230M RTN MXFP8 | 0.3878 | 0.3162 | 0.4622 | 0.5245 | 0.4139 | 0.8322 | 0.2459 | 306.7 MB |
+| Nomic v1.5 service | 0.5439 | 0.4968 | 0.5954 | 0.6236 | 0.5595 | 0.9289 | 0.3617 | service |
+| Jina calibrated MXFP4 | 0.6645 | 0.6133 | 0.7221 | 0.7571 | 0.6832 | 0.9462 | 0.5057 | 1167.7 MB |
+
+A separate BF16 cross-runtime run on `NVIDIA GeForce RTX 3090 Ti` with PyTorch `2.13.0+cu130` produced MRR 0.3963, 755.7 queries/s, 190.8 passages/s, and 736.2 MB peak CUDA allocation. CUDA throughput is reported separately and is not compared directly with Metal.
+
+A paired 10,000-sample bootstrap estimates calibrated MXFP8 minus BF16 MRR at -0.0026, with a 95% interval of [-0.0042, -0.0010]. A point estimate whose interval crosses zero is not presented as a
+quality win.
+
+## Usage
+
+```bash
+git clone https://github.com/RESMP-DEV/calibrated-code-embeddings
+cd calibrated-code-embeddings
+uv sync --extra mlx
+CODE_EMBEDDING_MODEL_PATH=/path/to/this-model code-embedding-serve --port 1235
+```
+
+The service exposes `POST /v1/embeddings`. It runs the bidirectional LFM2.5 body
+directly with MLX; LM Studio is not required. Prefix retrieval queries with `query: `
+and candidate code with `passage: ` when calling the model directly.
+
+## Training and data receipts
+
+Full-backbone symmetric in-batch InfoNCE training used 24,626 language-balanced pairs,
+two epochs, batch size 32, learning rate 2e-5, temperature 0.05, and seed 17. The
+training report records the NVIDIA RTX A6000 runtime and validation history.
+
+- `train`: 42,626 rows, SHA-256 `426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629`
+- `validation`: 5,319 rows, SHA-256 `9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096`
+- `test`: 6,995 rows, SHA-256 `9ed10842a12132b6bfb5421df1e2f88dbcfbf6f6e960f36b22eb9ea6e3c72315`
+- `calibration`: 4,096 rows, SHA-256 `ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9`
+
+The corpus combines pinned CodeSearchNet data with pinned permissively licensed code
+repositories. Exact and token 8-gram near-duplicates were removed with test-before-
+validation-before-train precedence. See `corpus_receipt.json`, `source_receipt.json`,
+`training_report.json`, `quantization_report.json` when present, `benchmarks/`, and
+`artifact_manifest.json` for machine-readable evidence.
+
+## License and attribution
+
+The weights retain the LFM Open License v1.0 in `LICENSE`, including its attribution and
+commercial-use conditions. `MODIFICATIONS.md` identifies RESMP.DEV's changes. The
+[training and quantization workbench](https://github.com/RESMP-DEV/calibrated-code-embeddings)
+is separately MIT licensed.
+
+## Citation
+
+```bibtex
+@article{liquidAI2026Encoders,
+  author = {Liquid AI},
+  title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
+  journal = {Liquid AI Blog},
+  year = {2026},
+  note = {www.liquid.ai/blog/lfm2-5-encoders},
+}
+```

artifact_manifest.json

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benchmarks/jina-code-calibrated-4k.json

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+}

benchmarks/nomic-v1.5-4k.json

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+{
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+  "dimensions": 768,
+  "endpoint": "http://127.0.0.1:1234/v1/embeddings",
+  "input_limits": {
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+    "max_query_chars": 1200
+  },
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+    "ruby": 750,
+    "rust": 750,
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+    "typescript": 750,
+    "yaml": 13
+  },
+  "model": "text-embedding-nomic-embed-text-v1.5",
+  "passage_prefix": "search_document: ",
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+}

chat_template.jinja

@@ -0,0 +1,64 @@
+{{- bos_token -}}
+{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
+{%- set ns = namespace(system_prompt="") -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- set sys_content = messages[0]["content"] -%}
+    {%- if sys_content is not string -%}
+        {%- for item in sys_content -%}
+            {%- if item["type"] == "text" -%}
+                {%- set ns.system_prompt = ns.system_prompt + item["text"] -%}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {%- set ns.system_prompt = sys_content -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- set ns.last_assistant_index = -1 -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "assistant" -%}
+        {%- set ns.last_assistant_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message["role"] + "\n" -}}
+    {%- set content = message["content"] -%}
+    {%- if content is not string -%}
+        {%- set ns.content = "" -%}
+        {%- for item in content -%}
+            {%- if item["type"] == "image" -%}
+                {%- set ns.content = ns.content + "<image>" -%}
+            {%- elif item["type"] == "text" -%}
+                {%- set ns.content = ns.content + item["text"] -%}
+            {%- else -%}
+                {%- set ns.content = ns.content + item | tojson -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- set content = ns.content -%}
+    {%- endif -%}
+    {%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
+        {%- if "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+    {%- endif -%}
+    {{- content + "<|im_end|>\n" -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "Lfm2BidirectionalModel"
+  ],
+  "auto_map": {
+    "AutoModel": "modeling_lfm2_bidirectional.Lfm2BidirectionalModel",
+    "AutoModelForMaskedLM": "modeling_lfm2_bidirectional.Lfm2BidirectionalForMaskedLM"
+  },
+  "block_auto_adjust_ff_dim": false,
+  "block_dim": 1024,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 1,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "conv_dim": 1024,
+  "conv_use_xavier_init": true,
+  "dtype": "bfloat16",
+  "eos_token_id": 7,
+  "full_attn_idxs": null,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_heads": 16,
+  "num_hidden_layers": 14,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.14.1",
+  "use_cache": false,
+  "use_pos_enc": true,
+  "vocab_size": 65536,
+  "quantization": {
+    "group_size": 32,
+    "bits": 4,
+    "mode": "mxfp4",
+    "algorithm": "activation_calibrated_block_gptq"
+  }
+}

corpus_receipt.json

@@ -0,0 +1,106 @@
+{
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+      "rows": 4096,
+      "sha256": "ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9"
+    },
+    "duplicates.jsonl": {
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+      "sha256": "2ddc5b1b0eaadffd9b9e7b7556f4a756c6d0b0857a509236ae3e090a9d17ad16"
+    },
+    "test.jsonl": {
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+      "sha256": "9ed10842a12132b6bfb5421df1e2f88dbcfbf6f6e960f36b22eb9ea6e3c72315"
+    },
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+      "sha256": "426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629"
+    },
+    "validation.jsonl": {
+      "rows": 5319,
+      "sha256": "9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096"
+    }
+  },
+  "codesearchnet": {
+    "languages": [
+      "go",
+      "java",
+      "javascript",
+      "php",
+      "python",
+      "ruby"
+    ],
+    "local_root": "/Users/kearm/datasets/calibrated-code-embeddings/code-search-net",
+    "repository": "code-search-net/code_search_net",
+    "revision": "bd0cf261e357a3eb5c8fba490d23ec1a1cd59555"
+  },
+  "configuration": {
+    "calibration_rows": 4096,
+    "calibration_sampling": "deterministic_language_round_robin_v2",
+    "evaluation_precedence_during_dedup": [
+      "test",
+      "validation",
+      "train"
+    ],
+    "near_duplicate_threshold": 0.82,
+    "ngram_size": 8,
+    "per_repo_language": 3000,
+    "test": 750,
+    "train": 6000,
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+  "counts": {
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+    "validation/typescript/pinned_repository": 750,
+    "validation/yaml/pinned_repository": 61
+  },
+  "input_pairs": 118039,
+  "local_source": "/Users/kearm/datasets/calibrated-code-embeddings/source-repos",
+  "repo_manifest": "/Users/kearm/AlphaHENG/contrib/calibrated-code-embeddings/sources/repos.json",
+  "schema_version": 1,
+  "selected_pairs": 54940,
+  "unique_pairs": 116638
+}

embedding_config.json

@@ -0,0 +1,7 @@
+{
+  "pooling": "attention_mask_mean",
+  "normalize": true,
+  "dimensions": 1024,
+  "query_prefix": "query: ",
+  "passage_prefix": "passage: "
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3c6540a9580560630ec5a5179c71872c16eff32f633e234114df814cb3a3a94
+size 220693600

modeling_lfm2_bidirectional.py

@@ -0,0 +1,211 @@
+"""LFM2 backbone with bidirectional attention + non-causal short-conv.
+
+Wired into the HF repo via `auto_map` in config.json so that
+
+AutoModel.from_pretrained(repo, trust_remote_code=True)
+AutoModelForMaskedLM.from_pretrained(repo, trust_remote_code=True)
+
+both return a model with the encoder-style patches already applied.
+
+Supports `attn_implementation` in {"eager", "sdpa", "flash_attention_2"}:
+
+eager/sdpa consume a 4D additive pad-only mask and reproduce the exact
+training-time behavior; flash_attention_2 receives the 2D padding mask (or
+None) and runs the kernel non-causally via `Lfm2Attention.is_causal = False`,
+yielding outputs equivalent to the unpadded forward.
+"""
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.lfm2 import modeling_lfm2 as _lfm2_mod
+from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
+from transformers.models.lfm2.modeling_lfm2 import (
+    Lfm2Attention,
+    Lfm2Model,
+    Lfm2PreTrainedModel,
+    Lfm2ShortConv,
+    apply_mask_to_padding_states,
+)
+
+
+def _bidirectional_mask(
+    config,
+    input_embeds: torch.Tensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    past_key_values=None,
+    position_ids: Optional[torch.LongTensor] = None,
+    **kwargs,
+) -> Optional[torch.Tensor]:
+    # transformers has renamed the embeds kwarg across versions
+    # (input_embeds <-> inputs_embeds); accept either to stay forward-compatible.
+    if input_embeds is None:
+        input_embeds = kwargs.get("inputs_embeds")
+
+    if config._attn_implementation == "flash_attention_2":
+        # FA2 only uses the 2D padding mask to unpad sequences; causality is
+        # controlled by `Lfm2Attention.is_causal` (set to False below).
+        if attention_mask is not None and not attention_mask.all():
+            return attention_mask
+        return None
+
+    device = input_embeds.device
+    dtype = input_embeds.dtype
+    bsz, q_len = input_embeds.shape[:2]
+    past = past_key_values.get_seq_length() if past_key_values is not None else 0
+    kv_len = past + q_len
+
+    mask = torch.zeros((bsz, 1, q_len, kv_len), device=device, dtype=dtype)
+    if attention_mask is not None:
+        cur_len = attention_mask.size(-1)
+        key_pad_flags = (attention_mask == 0).to(device=device, dtype=torch.float32)
+        pad_vec = torch.zeros((bsz, kv_len), device=device, dtype=torch.float32)
+        if cur_len > 0:
+            pad_vec[:, past:past + cur_len] = key_pad_flags * -1e9
+        mask = mask + pad_vec.to(dtype)[:, None, None, :]
+    return mask
+
+
+def _noncausal_shortconv_forward(
+    self,
+    hidden_states: torch.Tensor,
+    past_key_values=None,
+    cache_position=None,
+    attention_mask: Optional[torch.Tensor] = None,
+    **kwargs,
+) -> torch.Tensor:
+    x = apply_mask_to_padding_states(hidden_states, attention_mask)
+
+    BCx = self.in_proj(x).transpose(-1, -2)
+    B, C, x = BCx.chunk(3, dim=-2)
+    Bx = B * x
+
+    k = self.conv.weight.shape[-1]
+    pad = k // 2
+    conv_out = F.conv1d(
+        Bx, weight=self.conv.weight, bias=self.conv.bias,
+        stride=1, padding=pad, dilation=1, groups=Bx.shape[1],
+    )
+    if conv_out.shape[-1] > Bx.shape[-1]:
+        conv_out = conv_out[..., :Bx.shape[-1]]
+    elif conv_out.shape[-1] < Bx.shape[-1]:
+        conv_out = F.pad(conv_out, (0, Bx.shape[-1] - conv_out.shape[-1]))
+
+    y = C * conv_out
+    y = y.transpose(-1, -2).contiguous()
+    return self.out_proj(y)
+
+
+def _shortconv_forward(self, *args, **kwargs):
+    return self.slow_forward(*args, **kwargs)
+
+
+_PATCHED = False
+
+
+def _install_patches() -> None:
+    global _PATCHED
+    if _PATCHED:
+        return
+    _lfm2_mod.create_causal_mask = _bidirectional_mask
+    Lfm2ShortConv.slow_forward = _noncausal_shortconv_forward
+    Lfm2ShortConv.forward = _shortconv_forward
+    _PATCHED = True
+
+
+_install_patches()
+
+
+def _set_attention_noncausal(model) -> None:
+    for module in model.modules():
+        if isinstance(module, Lfm2Attention):
+            module.is_causal = False
+
+
+class Lfm2BidirectionalModel(Lfm2Model):
+    """LFM2 patched for encoder-style use: 
+    full bidirectional attention + non-causal short-conv."""
+
+    def __init__(self, config):
+        _install_patches()
+        super().__init__(config)
+        _set_attention_noncausal(self)
+
+
+class Lfm2BidirectionalForMaskedLM(Lfm2PreTrainedModel):
+    """LFM2 bidirectional encoder with a tied masked-LM head."""
+
+    config_class = Lfm2Config
+    base_model_prefix = "lfm2"
+    _tied_weights_keys = {"lm_head.weight": "lfm2.embed_tokens.weight"}
+
+    def __init__(self, config: Lfm2Config):
+        _install_patches()
+        config = type(config).from_dict({**config.to_dict(), "use_cache": False})
+        super().__init__(config)
+        self.lfm2 = Lfm2BidirectionalModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+        self.lm_head.weight = self.lfm2.embed_tokens.weight
+
+    def get_input_embeddings(self):
+        return self.lfm2.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.lfm2.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> MaskedLMOutput:
+        return_dict = True if return_dict is None else return_dict
+        outputs = self.lfm2(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=False,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+        hidden = outputs.last_hidden_state
+        logits = self.lm_head(hidden)
+
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(
+                logits.view(-1, self.config.vocab_size),
+                labels.view(-1),
+                ignore_index=-100,
+            )
+
+        if not return_dict:
+            out = (logits,) + outputs[1:]
+            return ((loss,) + out) if loss is not None else out
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

quantization_report.json

@@ -0,0 +1,1264 @@
+{
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+  "scope": "exact 32x32 Hessian and GPTQ error propagation within each MXFP4 group",
+  "source_model": "/home/ubuntu/LFM2.5-Encoder-230M-Code-BF16",
+  "source_revision": null,
+  "corpus_sha256": "ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9",
+  "calibration": {
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+    "scale_offsets": [
+      0,
+      -1,
+      -2,
+      -3,
+      -4,
+      1,
+      2
+    ]
+  },
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+  "weighted_error_calibrated": 356940396.227417,
+  "elapsed_seconds": 23.01606387999982,
+  "cuda": {
+    "device": "NVIDIA RTX A6000",
+    "torch": "2.13.0+cu130",
+    "peak_memory_bytes": 562977792
+  },
+  "layers": [
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