Update router model card and remote code

Adds the drafted model card, LFM Open License file, and remote-code routing class used by the README usage snippet.

LICENSE

@@ -0,0 +1,71 @@
+LFM Open License v1.0 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
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+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by this document.
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+(a) You must give any other recipients of the Work or Derivative Works a copy of this License; and
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+(b) You must cause any modified files to carry prominent notices stating that You changed the files; and
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+(c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
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+(d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
+
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
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+5. Commercial Use Limitation.
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+(a) The rights granted under this License for Commercial Use are conditioned upon You or Your Legal Entity not exceeding the Threshold.
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+END OF TERMS AND CONDITIONS

README.md

@@ -0,0 +1,99 @@
+---
+language:
+- en
+- de
+- es
+- fr
+- it
+- nl
+- pl
+- pt
+- ar
+- hi
+- ja
+- ru
+- tr
+- vi
+- zh
+tags:
+- liquid
+- lfm2
+- lfm2.5
+- bidirectional
+- masked-lm
+- encoder
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+pipeline_tag: text-classification
+base_model:
+    - LiquidAI/LFM2.5-Encoder-350M
+---
+
+<div align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI" 
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
+    <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • 
+    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
+    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • 
+    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+  </div>
+</div>
+
+# LFM2.5-Encoder-350-Prompt-Router
+
+A full fine-tune of [LFM2.5-Encoder-350M](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) with a zero-shot routing head that scores a prompt against user-defined routing lanes in a single encoder pass.
+
+Find more details about our encoders in our [blog post](https://www.liquid.ai/blog/lfm2-5-encoders).
+
+> [!NOTE]
+> 💻 **Demos**: Try this fine-tuned model running in a CPU-only Hugging Face space:
+> **[Zero-shot prompt routing](https://huggingface.co/spaces/LiquidAI/prompt-routing)** — define your own routing lanes as free text. The model scores the whole prompt against every lane in one pass.
+
+## Usage
+
+> ⚠️ Loads custom code via `trust_remote_code=True` (the model wraps a `trust_remote_code` encoder).
+
+Install the required packages:
+
+```bash
+pip install torch transformers
+```
+
+Run zero-shot prompt routing:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_id = "LiquidAI/LFM2.5-Encoder-350-Prompt-Router"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval()
+
+routes = ["Coding", "Sales", "Creative writing", "General knowledge"]
+prompt = "Can you help me debug a failing Python unit test?"
+
+print(model.route(prompt, routes, tokenizer=tokenizer))
+```
+
+## 📬 Contact
+
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
+
+## 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},
+}
+```

config.json

@@ -3,8 +3,8 @@
     "Lfm2BidirForSequenceRouting"
   ],
   "auto_map": {
-    "AutoModel": "modeling_lfm2_bidir_theirs.Lfm2BidirectionalModel_theirs",
-    "AutoModelForMaskedLM": "modeling_lfm2_bidir_theirs.Lfm2BidirForMaskedLM_theirs"
+    "AutoModel": "modeling_lfm2_bidirectional.Lfm2BidirForSequenceRouting",
+    "AutoModelForMaskedLM": "modeling_lfm2_bidirectional.Lfm2BidirectionalForMaskedLM"
   },
   "block_auto_adjust_ff_dim": true,
   "block_dim": 1024,

modeling_lfm2_bidirectional.py

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+"""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.
+"""
+
+import math
+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,
+        )
+
+
+class Lfm2BidirForSequenceRouting(Lfm2PreTrainedModel):
+    """Zero-shot prompt router built on the bidirectional LFM2 encoder."""
+
+    config_class = Lfm2Config
+    base_model_prefix = "lfm2"
+
+    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)
+        proj_dim = getattr(config, "rule_proj_dim", 256)
+        self.tok_proj = nn.Linear(config.hidden_size, proj_dim)
+        self.rule_proj = nn.Linear(config.hidden_size, proj_dim)
+        self.score_bias = nn.Parameter(torch.tensor(0.0))
+        self.logit_scale = nn.Parameter(torch.tensor(1.0))
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.lfm2.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.lfm2.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        text_pool: Optional[torch.Tensor] = None,
+        category_pool: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        outputs = self.lfm2(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            use_cache=False,
+            return_dict=True,
+        )
+        hidden = outputs.last_hidden_state
+        text_rep = torch.bmm(text_pool, hidden).squeeze(1)
+        category_rep = torch.bmm(category_pool, hidden)
+        query = F.normalize(self.tok_proj(text_rep), dim=-1)
+        categories = F.normalize(self.rule_proj(category_rep), dim=-1)
+        scale = torch.clamp(self.logit_scale.exp(), max=30.0)
+        logits = torch.einsum("bd,brd->br", query, categories) * scale + self.score_bias
+        return {"logits": logits}
+
+    @staticmethod
+    def _prefix(routes):
+        body = "\n".join(f"- {route}" for route in routes) if routes else "- (none)"
+        return f"Categories:\n{body}\n\nText:\n"
+
+    @staticmethod
+    def _category_ranges(routes):
+        ranges = []
+        pos = len("Categories:\n")
+        for route in routes:
+            start = pos + 2
+            end = start + len(route)
+            ranges.append((start, end))
+            pos = end + 1
+        return ranges
+
+    @torch.no_grad()
+    def route(self, text, routes, tokenizer, threshold=None):
+        prefix = self._prefix(routes)
+        full_text = prefix + text
+        enc = tokenizer(full_text, return_offsets_mapping=True, return_tensors="pt")
+        offsets = enc.pop("offset_mapping")[0].tolist()
+        enc = {k: v.to(self.device) for k, v in enc.items()}
+
+        text_start = len(prefix)
+        text_idxs = [
+            i for i, (start, end) in enumerate(offsets)
+            if end > text_start and start != end
+        ]
+        text_pool = torch.zeros(1, 1, len(offsets), device=self.device)
+        if text_idxs:
+            text_pool[0, 0, text_idxs] = 1 / len(text_idxs)
+
+        category_pool = torch.zeros(1, len(routes), len(offsets), device=self.device)
+        for route_idx, (start, end) in enumerate(self._category_ranges(routes)):
+            token_idxs = [
+                i for i, (tok_start, tok_end) in enumerate(offsets)
+                if tok_start < end and tok_end > start and tok_start != tok_end
+            ]
+            if token_idxs:
+                category_pool[0, route_idx, token_idxs] = 1 / len(token_idxs)
+
+        logits = self(**enc, text_pool=text_pool, category_pool=category_pool)["logits"][0]
+        probs = logits.softmax(dim=-1).detach().cpu()
+        results = [
+            {"route": route, "score": float(prob)}
+            for route, prob in zip(routes, probs)
+            if threshold is None or prob >= threshold
+        ]
+        return sorted(results, key=lambda item: item["score"], reverse=True)