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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Microsoft Corporation.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE

README.md

@@ -1,3 +1,144 @@
----
-license: apache-2.0
----
+---
+license: mit
+license_link: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+- bitnet
+- text-generation
+- large-language-model
+library_name: transformers
+---
+
+# BitNet b1.58 2B4T - Scaling Native 1-bit LLM
+
+This repository contains the weights for **BitNet b1.58 2B4T**, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale, developed by Microsoft Research.
+
+Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
+
+➡️ **Technical Report:** [BitNet b1.58 2B4T Technical Report](https://arxiv.org/abs/2504.12285)
+
+➡️ **Official Inference Code:** [microsoft/BitNet (bitnet.cpp)](https://github.com/microsoft/BitNet)
+
+## Model Variants
+
+Several versions of the model weights are available on Hugging Face:
+
+* [**`microsoft/bitnet-b1.58-2B-4T`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T) (This repository): Contains the packed 1.58-bit weights optimized for efficient inference. **Use this for deployment.**
+
+* [**`microsoft/bitnet-b1.58-2B-4T-bf16`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-bf16): Contains the master weights in BF16 format. **Use this only for training or fine-tuning purposes.**
+
+* [**`microsoft/bitnet-b1.58-2B-4T-gguf`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf): Contains the model weights in GGUF format, compatible with the `bitnet.cpp` library for CPU inference.
+
+## Model Details
+
+* **Architecture:** Transformer-based, modified with `BitLinear` layers (BitNet framework).
+    * Uses Rotary Position Embeddings (RoPE).
+    * Uses squared ReLU (ReLU²) activation in FFN layers.
+    * Employs [`subln`](https://proceedings.mlr.press/v202/wang23u.html) normalization.
+    * No bias terms in linear or normalization layers.
+* **Quantization:** Native 1.58-bit weights and 8-bit activations (W1.58A8).
+    * Weights are quantized to ternary values {-1, 0, +1} using absmean quantization during the forward pass.
+    * Activations are quantized to 8-bit integers using absmax quantization (per-token).
+    * **Crucially, the model was *trained from scratch* with this quantization scheme, not post-training quantized.**
+* **Parameters:** ~2 Billion
+* **Training Tokens:** 4 Trillion
+*   **Context Length:** Maximum sequence length of **4096 tokens**.
+    *   *Recommendation:* For optimal performance on tasks requiring very long contexts (beyond the pre-training length or for specialized long-reasoning tasks), we recommend performing intermediate long-sequence adaptation/training before the final fine-tuning stage.
+* **Training Stages:**
+    1.  **Pre-training:** Large-scale training on public text/code and synthetic math data using a two-stage learning rate and weight decay schedule.
+    2.  **Supervised Fine-tuning (SFT):** Fine-tuned on instruction-following and conversational datasets using sum loss aggregation and specific hyperparameter tuning.
+    3.  **Direct Preference Optimization (DPO):** Aligned with human preferences using preference pairs.
+* **Tokenizer:** LLaMA 3 Tokenizer (vocab size: 128,256).
+
+## How to Use (with `transformers`)
+
+**VERY IMPORTANT NOTE ON EFFICIENCY**
+
+> Please do NOT expect performance efficiency gains (in terms of speed, latency, or energy consumption) when using this model with the standard transformers library, even with the required fork.
+>
+> The current execution paths within transformers do not contain the specialized, highly optimized computational kernels required to leverage the advantages of the BitNet architecture. Running the model via transformers will likely result in inference speeds and energy usage comparable to, or potentially worse than, standard full-precision models within this framework on both CPU and GPU.
+>
+> While you might observe reduced memory usage due to the quantized weights, the primary computational efficiency benefits are not accessible through this standard transformers usage path.
+>
+> For achieving the efficiency benefits demonstrated in the technical paper, you MUST use the dedicated C++ implementation: [bitnet.cpp](https://github.com/microsoft/BitNet).
+
+### Requirements
+
+```bash
+pip install git+https://github.com/shumingma/transformers.git
+```
+We are actively working with the Hugging Face team to integrate the necessary code into the main `transformers` library. This installation method may change in the future.
+
+### Example
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "microsoft/bitnet-b1.58-2B-4T"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16
+)
+
+# Apply the chat template
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "How are you?"},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+chat_input = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate response
+chat_outputs = model.generate(**chat_input, max_new_tokens=50)
+response = tokenizer.decode(chat_outputs[0][chat_input['input_ids'].shape[-1]:], skip_special_tokens=True) # Decode only the response part
+print("\nAssistant Response:", response)
+```
+
+## How to Use (with `bitnet.cpp`)
+
+Please refer to the [bitnet.cpp](https://github.com/microsoft/BitNet) GitHub repository for detailed compilation steps, usage examples, and command-line options.
+
+## Evaluation
+
+BitNet b1.58 2B4T was evaluated against leading open-weight full-precision LLMs of similar size. Below are the key results (all models are instruction-tuned versions):
+
+| Benchmark             | LLaMA 3.2 1B | Gemma-3 1B | Qwen2.5 1.5B | SmolLM2 1.7B | MiniCPM 2B | **BitNet b1.58 2B** |
+|--------------------------------|--------------|------------|--------------|--------------|------------|---------------------|
+| **Memory (Non-emb)** | 2GB          | 1.4GB      | 2.6GB        | 3.2GB        | 4.8GB      | **0.4GB** |
+| **Latency (CPU Decoding)** | 48ms         | 41ms       | 65ms         | 67ms         | 124ms      | **29ms** |
+| **Energy (Estimated)** | 0.258J       | 0.186J     | 0.347J       | 0.425J       | 0.649J     | **0.028J** |
+| **Training Tokens (Pre-train)**| 9T* | 2T** | 18T          | 11T          | 1.1T       | 4T                  |
+| ARC-Challenge   | 37.80        | 38.40      | 46.67        | 43.52        | 44.80      | **49.91** |
+| ARC-Easy        | 63.17        | 63.13      | **76.01** | 62.92        | 72.14      | 74.79               |
+| OpenbookQA      | 34.80        | 38.80      | 40.80        | **46.00** | 40.20      | 41.60               |
+| BoolQ                | 64.65        | 74.22      | 78.04        | 75.78        | **80.67** | 80.18               |
+| HellaSwag       | 60.80        | 57.69      | 68.28        | **71.71** | 70.81      | 68.44               |
+| PIQA            | 74.21        | 71.93      | 76.12        | 76.12        | 76.66      | **77.09** |
+| WinoGrande           | 59.51        | 58.48      | 62.83        | 68.98        | 61.80      | **71.90** |
+| CommonsenseQA       | 58.48        | 42.10      | **76.41** | 63.55        | 71.74      | 71.58               |
+| TruthfulQA          | 43.80        | 38.66      | **46.67** | 39.90        | 41.41      | 45.31               |
+| TriviaQA              | 37.60        | 23.49      | 38.37        | **45.97** | 34.13      | 33.57               |
+| MMLU                 | 45.58        | 39.91      | **60.25** | 49.24        | 51.82      | 53.17               |
+| HumanEval+        | 31.10        | 37.20      | **50.60** | 28.00        | 43.90      | 38.40               |
+| GSM8K                 | 38.21        | 31.16      | 56.79        | 45.11        | 4.40       | **58.38** |
+| MATH-500              | 23.00        | 42.00      | **53.00** | 17.60        | 14.80      | 43.40               |
+| IFEval   | 62.71        | **66.67** | 50.12        | 57.91        | 36.81      | 53.48               |
+| MT-bench         | 5.43         | 6.40       | 6.12         | 5.50         | **6.57** | 5.85                |
+| **Average** | 44.90        | 43.74      | **55.23** | 48.70        | 42.05      | 54.19               |
+
+*LLaMA 3.2 1B uses pruning & distillation.
+
+**Gemma-3 1B uses distillation.
+
+## License
+The model weights and code are released under the [MIT License](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T/blob/main/LICENSE).
+
+## Disclaimer
+This model is intended for research and development purposes. While efforts have been made to align it using SFT and DPO, it may still produce outputs that are unexpected, biased, or inaccurate. Please use responsibly.

config.json

@@ -0,0 +1,30 @@
+{
+    "architectures": [
+      "BitNetForCausalLM"
+    ],
+    "auto_map": {
+      "AutoConfig": "configuration_bitnet.BitNetConfig",
+      "AutoModelForCausalLM": "modeling_bitnet.BitNetForCausalLM"
+    },
+    "bos_token_id": 128000,
+    "eos_token_id": 128001,
+    "hidden_act": "relu2",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 6912,
+    "max_position_embeddings": 4096,
+    "model_type": "bitnet",
+    "rms_norm_eps": 1e-05,
+    "num_attention_heads": 20,
+    "num_hidden_layers": 30,
+    "num_key_value_heads": 5,
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "vocab_size": 128256,
+    "quantization_config": {
+      "quant_method": "bitnet",
+      "offline_quantization": true
+    }
+}

configuration_bitnet.py

@@ -0,0 +1,188 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class BitnetConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BitnetModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BitnetModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Bitnet 1 supports up to 2048 tokens,
+            Bitnet 2 up to 4096, CodeBitnet up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+    ```python
+    >>> from transformers import BitnetModel, BitnetConfig
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = BitnetConfig()
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = BitnetModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        weight_bits=1,
+        input_bits=8,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.weight_bits = weight_bits
+        self.input_bits = input_bits
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128009
+  ],
+  "do_sample": true,
+  "temperature": 0.6,
+  "max_length": 4096,
+  "top_p": 0.9,
+  "transformers_version": "4.40.0.dev0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8143ae115ed6babe5e5ada8fb8c5b769d8f417802b2db042ad98b4f7ed73975b
+size 1178623988

special_tokens_map.json

@@ -0,0 +1,4 @@
+{
+  "bos_token": "<|begin_of_text|>",
+  "eos_token": "<|end_of_text|>"
+}

tokenizer_config.json

@@ -0,0 +1,2062 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128008": {
+      "content": "<|reserved_special_token_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128009": {
+      "content": "<|eot_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128010": {
+      "content": "<|reserved_special_token_5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128011": {
+      "content": "<|reserved_special_token_6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128012": {
+      "content": "<|reserved_special_token_7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128013": {
+      "content": "<|reserved_special_token_8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128014": {
+      "content": "<|reserved_special_token_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128015": {
+      "content": "<|reserved_special_token_10|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['role'] | capitalize + ': '+ message['content'] | trim + '<|eot_id|>' %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant: ' }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}