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@@ -0,0 +1,41 @@
+LICENSE TEXT
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+LFM Open License v1.0 
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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README.md

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+---
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+- ar
+- zh
+- fr
+- de
+- ja
+- ko
+- es
+pipeline_tag: text-generation
+tags:
+- liquid
+- lfm2
+- edge
+---
+
+<center>
+<div style="text-align: center;">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/7_6D7rWrLxp2hb6OHSV1p.png" 
+    alt="Liquid AI"
+    style="width: 100%; max-width: 66%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+</div>
+
+<a href="https://playground.liquid.ai/chat">
+<svg width="114.8" height="20" viewBox="0 0 1300 200" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Liquid Playground" style="margin-bottom: 1em;">
+  <title>Liquid: Playground</title>
+  <g>
+    <rect fill="#fff" width="600" height="200"></rect>
+    <rect fill="url(#x)" x="600" width="700" height="200"></rect>
+  </g>
+  <g transform="translate(20, 30) scale(0.4, 0.4)">
+    <path d="M172.314 129.313L172.219 129.367L206.125 188.18C210.671 195.154 213.324 203.457 213.324 212.382C213.324 220.834 210.956 228.739 206.839 235.479L275.924 213.178L167.853 33.6L141.827 76.9614L172.314 129.313Z" fill="black"/>
+    <path d="M114.217 302.4L168.492 257.003C168.447 257.003 168.397 257.003 168.352 257.003C143.515 257.003 123.385 237.027 123.385 212.387C123.385 203.487 126.023 195.204 130.55 188.24L162.621 132.503L135.966 86.7327L60.0762 213.183L114.127 302.4H114.217Z" fill="black"/>
+    <path d="M191.435 250.681C191.435 250.681 191.43 250.681 191.425 250.686L129.71 302.4H221.294L267.71 226.593L191.435 250.686V250.681Z" fill="black"/>
+  </g>
+  <g aria-hidden="true" fill="#fff" text-anchor="start" font-family="Verdana,DejaVu Sans,sans-serif" font-size="110">
+    <text x="200" y="148" textLength="329" fill="#000" opacity="0.1">Liquid</text>
+    <text x="190" y="138" textLength="329" fill="#000">Liquid</text>
+    <text x="655" y="148" textLength="619" fill="#000" opacity="0.1">Playground</text>
+    <text x="645" y="138" textLength="619">Playground</text>
+  </g>
+  
+  <linearGradient id="x" x1="0%" y1="0%" x2="100%" y2="0%">
+    <stop offset="0%" style="stop-color:#000000"></stop>
+    <stop offset="100%" style="stop-color:#000000"></stop>
+  </linearGradient>
+</svg>
+</a>
+</center>
+
+# LFM2-1.2B
+
+LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. 
+
+We're releasing the weights of three post-trained checkpoints with 350M, 700M, and 1.2B parameters. They provide the following key features to create AI-powered edge applications:
+
+* **Fast training & inference** – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3.
+* **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities.
+* **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions.
+* **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.
+
+Find more information about LFM2 in our [blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
+
+## 📄 Model details
+
+Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. 
+They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. 
+However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
+
+| Property            | Value                         |
+| ------------------- | ----------------------------- |
+| **Parameters**      | 1.2B                          |
+| **Layers**          | 16 (10 conv + 6 attn)         |
+| **Context length**  | 32,768 tokens                 |
+| **Vocabulary size** | 65,536                        |
+| **Precision**       | bfloat16                      |
+| **Training budget** | 10 trillion tokens            |
+| **License**         | LFM Open License v1.0         |
+
+**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
+
+**Generation parameters**: We recommend the following parameters:
+* `temperature=0.3`
+* `min_p=0.15`
+* `repetition_penalty=1.05`
+
+**Chat template**: LFM2 uses a ChatML-like chat template as follows:
+
+```
+<|startoftext|><|im_start|>system
+You are a helpful assistant trained by Liquid AI.<|im_end|>
+<|im_start|>user
+What is C. elegans?<|im_end|>
+<|im_start|>assistant
+It's a tiny nematode that lives in temperate soil environments.<|im_end|>
+```
+
+You can apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.
+
+**Tool use**: It consists of four main steps:
+1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt
+2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer.
+3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role.
+4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
+
+Here is a simple example of a conversation using tool use:
+
+```
+<|startoftext|><|im_start|>system
+List of tools: <|tool_list_start|>[{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|tool_list_end|><|im_end|>
+<|im_start|>user
+What is the current status of candidate ID 12345?<|im_end|>
+<|im_start|>assistant
+<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
+<|im_start|>tool
+<|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|im_end|>
+<|im_start|>assistant
+The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>
+```
+
+**Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
+
+**Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.
+
+**Training approach**:
+* Knowledge distillation using [LFM1-7B](https://www.liquid.ai/blog/introducing-lfm-7b-setting-new-standards-for-efficient-language-models) as teacher model
+* Very large-scale SFT on 50% downstream tasks, 50% general domains
+* Custom DPO with length normalization and semi-online datasets
+* Iterative model merging
+
+## 🏃 How to run LFM2
+
+> [!WARNING]
+>  ⚠️ Until LFM2 support is merged into the transformers library, it requires setting `trust_remote_code=True` when loading the model.
+
+To run LFM2, you need Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.53.0.
+You can update or install it with the following command: `pip install transformers==4.53.0`
+
+Here is an example of how to generate an answer with transformers in Python:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_id = "LiquidAI/LFM2-1.2B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="bfloat16",
+    trust_remote_code=True,
+#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Generate answer
+prompt = "What is C. elegans?"
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+).to(model.device)
+
+output = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.3,
+    min_p=0.15,
+    repetition_penalty=1.05,
+    max_new_tokens=512,
+)
+
+print(tokenizer.decode(output[0], skip_special_tokens=False))
+
+# <|startoftext|><|im_start|>user
+# What is C. elegans?<|im_end|>
+# <|im_start|>assistant
+# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
+# nematode worm (roundworm) that belongs to the phylum Nematoda.
+```
+
+You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing).
+
+## 🔧 How to fine-tune LFM2
+
+We recommend fine-tuning LFM2 models on your use cases to maximize performance.
+
+| Notebook | Description | Link |
+|-------|------|------|
+| SFT + LoRA | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter in TRL. | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="120" alt="Colab link"></a> |
+| DPO | Preference alignment with Direct Preference Optimization (DPO) in TRL. | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="120" alt="Colab link"></a> |
+
+## 📈 Performance
+
+LFM2 outperforms similar-sized models across different evaluation categories.
+
+### 1. Automated benchmarks
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/3cB7VqMnrG9I8EqrL7k-q.png)
+
+| Model | MMLU | GPQA | IFEval | IFBench | GSM8K | MGSM | MMMLU |
+|-------|------|------|--------|---------|-------|------|-------|
+| LFM2-350M | 43.43 | 27.46 | 65.12 | 16.41 | 30.1 | 29.52 | 37.99 |
+| LFM2-700M | 49.9 | 28.48 | 72.23 | 20.56 | 46.4 | 45.36 | 43.28 |
+| LFM2-1.2B | *55.23* | **31.47** | **74.89** | *20.7* | *58.3* | *55.04* | **46.73** |
+| Qwen3-0.6B | 44.93 | 22.14 | 64.24 | 19.75 | 36.47 | 41.28 | 30.84 |
+| Qwen3-1.7B | **59.11** | 27.72 | *73.98* | **21.27** | 51.4 | **66.56** | *46.51* |
+| Llama-3.2-1B-Instruct | 46.6 | *28.84* | 52.39 | 16.86 | 35.71 | 29.12 | 38.15 |
+| gemma-3-1b-it | 40.08 | 21.07 | 62.9 | 17.72 | **59.59** | 43.6 | 34.43 |
+
+### 2. LLM-as-a-Judge
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4Yxx0l9aQ6ATrps5GWHzv.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/lzpZOGwH-8bTlOWd3tv6M.png)
+
+### 3. Inference
+
+#### Throughput comparison on CPU in ExecuTorch
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/KoKcsXUOnkvz2dwZ99k08.png)
+
+#### Throughput comparison on CPU in Llama.cpp
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/c7UYZ5nh6qJMB4rd6WKde.png)
+
+## 📬 Contact
+
+If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).

chat_template.jinja

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+{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

@@ -0,0 +1,50 @@
+{
+  "architectures": [
+    "LFM2ForCausalLM"
+  ],
+  "block_auto_adjust_ff_dim": true,
+  "block_dim": 1536,
+  "block_ff_dim": 10240,
+  "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": 1536,
+  "conv_dim_out": 1536,
+  "conv_use_xavier_init": true,
+  "eos_token_id": 7,
+  "full_attn_idxs": [
+    2,
+    5,
+    8,
+    10,
+    12,
+    14
+  ],
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 24,
+  "num_heads": 24,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_theta": 1000000.0,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.0.dev0",
+  "use_cache": true,
+  "use_pos_enc": true,
+  "vocab_size": 65536,
+  "auto_map": {
+    "AutoConfig": "modeling_lfm2.LFM2Config",
+    "AutoModelForCausalLM": "modeling_lfm2.LFM2ForCausalLM"
+  }
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 7,
+  "pad_token_id": 0,
+  "transformers_version": "4.53.0.dev0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7b52669217ecb538740187f41ce2a5802afa1c8d81d8d153b847bbd21d1bdda
+size 1484995328

modeling_lfm2.py

@@ -0,0 +1,924 @@
+from typing import Any, Callable, ClassVar, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.cache_utils import DynamicCache
+from transformers.configuration_utils import PretrainedConfig
+from transformers.generation import GenerationMixin
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging
+from transformers.utils.import_utils import is_causal_conv1d_available
+
+if is_causal_conv1d_available():
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+    causal_conv1d_fn, causal_conv1d_update = None, None
+
+
+kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
+is_fast_path_available = all(kernel_modules)
+
+logger = logging.get_logger(__name__)
+
+
+# ========================================================
+# Config Class (to be removed) once integrated into
+# `transformers`. For now, allows for dynamic importing.
+# ========================================================s
+# from .configuration_lfm2 import LFM2Config
+
+
+class LFM2Config(PretrainedConfig):
+    model_type = "lfm2"
+    keys_to_ignore_at_inference: ClassVar = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 65536,
+        hidden_size: int = 2560,
+        num_hidden_layers: int = 32,
+        pad_token_id: int = 0,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        tie_embedding: bool = True,
+        theta: float = 1000000.0,
+        max_position_embeddings: int = 128_000,
+        use_cache: bool = True,
+        norm_eps: float = 0.00001,
+        initializer_range: float = 0.02,
+        num_attention_heads: int = 32,
+        num_key_value_heads: int = 8,
+        conv_bias: bool = False,
+        conv_dim: int = 2560,
+        conv_L_cache: int = 3,
+        block_dim: int = 2560,
+        block_ff_dim: int = 12288,
+        block_multiple_of: int = 256,
+        block_ffn_dim_multiplier: float = 1.0,
+        block_auto_adjust_ff_dim: bool = True,
+        full_attn_idxs: Optional[list[int]] = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.rope_theta = theta
+        self.max_position_embeddings = max_position_embeddings
+        self.use_cache = use_cache
+        self.norm_eps = norm_eps
+        self.initializer_range = initializer_range
+
+        # attn operator config
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.full_attn_idxs = full_attn_idxs
+
+        # custom operator config
+        self.conv_bias = conv_bias
+        self.conv_dim = conv_dim
+        self.conv_L_cache = conv_L_cache
+
+        # block config
+        self.block_dim = block_dim
+        self.block_ff_dim = block_ff_dim
+        self.block_multiple_of = block_multiple_of
+        self.block_ffn_dim_multiplier = block_ffn_dim_multiplier
+        self.block_auto_adjust_ff_dim = block_auto_adjust_ff_dim
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_embedding,
+            **kwargs,
+        )
+
+    @property
+    def layers_block_type(self):
+        return ["attention" if i in self.full_attn_idxs else "conv" for i in range(self.num_hidden_layers)]
+
+
+class LFM2RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float())
+        return output.type_as(x) * self.weight
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LFM2RotaryEmbedding(nn.Module):
+    def __init__(self, config: LFM2Config, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    num_key_value_groups = query.shape[1] // key.shape[1]
+    key_states = repeat_kv(key, num_key_value_groups)
+    value_states = repeat_kv(value, num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+    else:
+        seq_len = key_states.shape[-2]
+        causal_mask = torch.triu(
+            torch.full((seq_len, seq_len), float("-inf"), device=attn_weights.device),
+            diagonal=1,
+        )
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class LFM2MLP(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        ff_dim: int,
+        multiple_of: int,
+        auto_adjust_ff_dim: bool,
+        ffn_dim_multiplier: Optional[float],
+    ):
+        super().__init__()
+        if auto_adjust_ff_dim:
+            ff_dim = int(2 * ff_dim / 3)
+            # custom dim factor multiplier
+            if ffn_dim_multiplier is not None:
+                ff_dim = int(ffn_dim_multiplier * ff_dim)
+            ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, ff_dim, bias=False)
+        self.w3 = nn.Linear(dim, ff_dim, bias=False)
+        self.w2 = nn.Linear(ff_dim, dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class LFM2Cache(DynamicCache):
+    """
+    Attention and conv cache for LFM2.
+
+    It stores the Key and Value states as a list of tensors, one for each layer.
+    Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
+    Conv layer cache shape: `[batch_size, conv_dim, L_cache-1]`.
+    """
+
+    def __init__(
+        self,
+        config: LFM2Config,
+        max_batch_size: int,
+        dtype: torch.dtype = torch.float32,
+        device: Union[torch.device, str, None] = None,
+    ):
+        super().__init__()  # initialize key and value cache
+        self.max_batch_size = max_batch_size
+        self.full_attn_idxs = config.full_attn_idxs
+        self.conv_L_cache = config.conv_L_cache
+        self._dtype = dtype
+
+        self.conv_cache: list[torch.Tensor] = []
+        device = torch.device(device) if device is not None else None
+
+        for _ in range(config.num_hidden_layers):
+            conv_state = torch.zeros(
+                self.max_batch_size,
+                config.conv_dim,
+                self.conv_L_cache,
+                dtype=self._dtype,
+                device=device,
+            )
+            torch._dynamo.mark_static_address(conv_state)
+            self.conv_cache.append(conv_state)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[dict[str, Any]] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        # Update the number of seen tokens
+        # if layer_idx == 0:
+        if layer_idx == self.full_attn_idxs[0]:
+            self._seen_tokens += key_states.shape[-2]
+
+        # Update the cache
+        if key_states is not None:
+            if len(self.key_cache) <= layer_idx:
+                # There may be skipped layers, fill them with empty lists
+                for _ in range(len(self.key_cache), layer_idx):
+                    self.key_cache.append(torch.tensor([]))
+                    self.value_cache.append(torch.tensor([]))
+                self.key_cache.append(key_states)
+                self.value_cache.append(value_states)
+            elif (
+                not self.key_cache[layer_idx].numel()  # prefers not t.numel() to len(t) == 0 to export the model
+            ):  # fills previously skipped layers; checking for tensor causes errors
+                self.key_cache[layer_idx] = key_states
+                self.value_cache[layer_idx] = value_states
+            else:
+                self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+                self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        for layer_idx in range(len(self.key_cache)):
+            device = self.key_cache[layer_idx].device
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+            device = self.value_cache[layer_idx].device
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+            device = self.conv_cache[layer_idx].device
+            self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # take any layer that contains cache and not empty tensor
+        layer_idx = self.full_attn_idxs[0] if layer_idx not in self.full_attn_idxs else layer_idx
+        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def reset(self):
+        for layer_idx in range(len(self.conv_cache)):
+            # In-place ops prevent breaking the static address
+            self.conv_cache[layer_idx].zero_()
+
+
+class LFM2Attention(nn.Module):
+    def __init__(self, config: LFM2Config, layer_idx: Optional[int] = None, **kwargs):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and "
+                "will lead to errors during the forward call if caching is used. Please make sure to provide a "
+                "`layer_idx` when creating this class."
+            )
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.is_causal = True
+
+        self.q_layernorm = LFM2RMSNorm(self.head_dim, eps=config.norm_eps)
+        self.k_layernorm = LFM2RMSNorm(self.head_dim, eps=config.norm_eps)
+
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+        self.out_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[LFM2Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        q = self.q_layernorm(self.q_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
+        k = self.k_layernorm(self.k_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
+        v = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            k, v = past_key_value.update(
+                key_states=k, value_states=v, layer_idx=self.layer_idx, cache_kwargs=cache_kwargs
+            )
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            q,
+            k,
+            v,
+            attention_mask,
+            dropout=0.0,
+            scaling=self.scaling,
+            **kwargs,
+        )
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        output = self.out_proj(attn_output)
+        return output, attn_weights
+
+
+class LFM2ShortConv(nn.Module):
+    def __init__(
+        self,
+        config: LFM2Config,
+        dim: int,
+        layer_idx: int,
+    ):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.L_cache = config.conv_L_cache
+        self.bias = config.conv_bias
+
+        self.conv = nn.Conv1d(
+            in_channels=dim,
+            out_channels=dim,
+            kernel_size=self.L_cache,
+            groups=dim,
+            bias=self.bias,
+            padding=self.L_cache - 1,
+        )
+        self.in_proj = nn.Linear(dim, 3 * dim, bias=self.bias)
+        self.out_proj = nn.Linear(dim, dim, bias=self.bias)
+
+    def cuda_kernels_forward(
+        self,
+        x: torch.Tensor,
+        cache_params: Optional[LFM2Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        BCx = self.in_proj(x).transpose(-1, -2)
+        B, C, x = BCx.chunk(3, dim=-2)
+
+        Bx = B * x
+
+        conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
+        if cache_params is not None and cache_position[0] > 0:
+            conv_out = causal_conv1d_update(
+                Bx.squeeze(-1),
+                cache_params.conv_cache[self.layer_idx],
+                conv_weights,
+                self.conv.bias,
+                None,
+            )
+            conv_out = conv_out.unsqueeze(-1)
+        else:
+            if cache_params is not None:
+                conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
+                cache_params.conv_cache[self.layer_idx].copy_(conv_state)
+
+            conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
+
+        y = C * conv_out
+        y = self.out_proj(y.transpose(-1, -2).contiguous())
+        return y
+
+    def slow_forward(
+        self,
+        x: torch.Tensor,
+        cache_params: Optional[LFM2Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        seqlen = x.shape[1]
+        BCx = self.in_proj(x).transpose(-1, -2)
+        B, C, x = BCx.chunk(3, dim=-2)
+
+        Bx = B * x
+
+        if cache_params is not None and cache_position[0] > 0:
+            conv_state = cache_params.conv_cache[self.layer_idx]
+            cache_position = cache_position.clamp(0, self.L_cache - 1)
+            conv_state = conv_state.roll(shifts=-1, dims=-1)
+            conv_state[:, :, cache_position] = Bx.to(device=conv_state.device, dtype=conv_state.dtype)
+            cache_params.conv_cache[self.layer_idx].copy_(conv_state)
+            conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
+            if self.bias:
+                conv_out += self.conv.bias
+
+            conv_out = conv_out.unsqueeze(-1)
+        else:
+            if cache_params is not None:
+                conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
+                cache_params.conv_cache[self.layer_idx].copy_(conv_state)
+
+            conv_out = self.conv(Bx)[..., :seqlen]
+
+        y = C * conv_out
+        y = y.transpose(-1, -2).contiguous()
+        y = self.out_proj(y)
+        return y
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cache_params: Optional[LFM2Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if is_fast_path_available and "cuda" in x.device.type and not torch._dynamo.is_compiling():
+            return self.cuda_kernels_forward(x, cache_params, cache_position, attention_mask)
+        return self.slow_forward(x, cache_params, cache_position, attention_mask)
+
+
+class LFM2AttentionDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: LFM2Config, layer_idx: int):
+        super().__init__()
+        self.self_attn = LFM2Attention(config, layer_idx)
+        self.feed_forward = LFM2MLP(
+            dim=config.block_dim,
+            ff_dim=config.block_ff_dim,
+            multiple_of=config.block_multiple_of,
+            auto_adjust_ff_dim=config.block_auto_adjust_ff_dim,
+            ffn_dim_multiplier=config.block_ffn_dim_multiplier,
+        )
+        self.operator_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
+        self.ffn_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        h, self_attn_weights = self.self_attn(
+            hidden_states=self.operator_norm(hidden_states),
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        h += hidden_states
+        out = h + self.feed_forward.forward(self.ffn_norm(h))
+
+        outputs = (out,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class LFM2ShortConvDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: LFM2Config, layer_idx: int):
+        super().__init__()
+        self.conv = LFM2ShortConv(
+            config=config,
+            dim=config.conv_dim,
+            layer_idx=layer_idx,
+        )
+        self.feed_forward = LFM2MLP(
+            dim=config.block_dim,
+            ff_dim=config.block_ff_dim,
+            multiple_of=config.block_multiple_of,
+            auto_adjust_ff_dim=config.block_auto_adjust_ff_dim,
+            ffn_dim_multiplier=config.block_ffn_dim_multiplier,
+        )
+        self.operator_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
+        self.ffn_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        past_key_value: Optional[LFM2Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        h = self.conv(
+            self.operator_norm(hidden_states),
+            cache_params=past_key_value,
+            cache_position=cache_position,
+            attention_mask=attention_mask,
+        )
+        self_attn_weights = None
+
+        h += hidden_states
+        out = h + self.feed_forward.forward(self.ffn_norm(h))
+
+        outputs = (out,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+@auto_docstring
+class LFM2PretrainedModel(PreTrainedModel):
+    config_class = LFM2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules: ClassVar = ["LFM2AttentionDecoderLayer", "LFM2ShortConvDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, (nn.Linear, nn.Conv1d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, LFM2RMSNorm):
+            module.weight.data.fill_(1.0)
+
+
+class LFM2Model(LFM2PretrainedModel):
+    def __init__(self, config: LFM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.pos_emb = LFM2RotaryEmbedding(config)
+
+        decoder_layers = []
+        for i in range(config.num_hidden_layers):
+            if i in config.full_attn_idxs:
+                decoder_layers.append(LFM2AttentionDecoderLayer(config, layer_idx=i))
+            else:
+                decoder_layers.append(LFM2ShortConvDecoderLayer(config, layer_idx=i))
+        self.layers = nn.ModuleList(decoder_layers)
+
+        self.embedding_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[LFM2Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            batch_size = inputs_embeds.shape[0]
+            past_key_values = LFM2Cache(
+                config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
+            )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+        )
+        hidden_states = inputs_embeds
+
+        position_embeddings = self.pos_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **flash_attn_kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.embedding_norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        output = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+        return output if return_dict else output.to_tuple()
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+@auto_docstring
+class LFM2ForCausalLM(LFM2PretrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: LFM2Config):
+        super().__init__(config)
+        self.model = LFM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.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 set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[LFM2Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> Union[tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            return_dict=return_dict,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        # Overwritten -- Support custom LFM2Cache.
+
+        empty_past_kv = past_key_values is None or (
+            isinstance(past_key_values, DynamicCache) and past_key_values._seen_tokens == 0
+        )
+
+        # Omit tokens covered by past_key_values.
+        if not empty_past_kv:
+            # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+            # Exception 1: when passing input_embeds, input_ids may be missing entries
+            # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+            # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
+            #              (we can't check exception 3 while compiling)
+            if (
+                inputs_embeds is not None  # Exception 1
+                or cache_position[-1] >= input_ids.shape[1]  # Exception 3
+            ):
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+        else:
+            past_key_values = LFM2Cache(self.config, input_ids.shape[0], dtype=self.dtype, device=self.device)
+
+        # if attention_mask is not None and position_ids is None:
+        #     # create position_ids on the fly for batch generation
+        #     position_ids = attention_mask.long().cumsum(-1) - 1
+        #     position_ids.masked_fill_(attention_mask == 0, 1)
+        #     if not empty_past_kv:
+        #         position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and empty_past_kv:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids.contiguous()}  # `contiguous()` needed for compilation use cases
+
+        model_inputs.update(
+            {
+                # "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+            }
+        )
+        return model_inputs
+
+
+__all__ = ["LFM2ForCausalLM", "LFM2Model", "LFM2PretrainedModel"]

requirements.txt

@@ -0,0 +1,2 @@
+transformers==4.53.0.dev0
+tokenizers==0.21.1

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,4074 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<|fim_pre|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<|fim_mid|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<|fim_suf|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<|tool_list_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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