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Model: PhantomAjusshi/phi3-auditor-merged
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- microsoft/Phi-3-mini-4k-instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- phi3
+- lora
+- peft
+- clinical-ai
+- model-audit
+- text-generation
+- fine-tuned
+- healthcare
+- safetensors
+---
+
+# 🏥 phi3-auditor-merged
+
+**Phi-3-mini fine-tuned for clinical AI model auditing.**
+
+This model takes a JSON object of ML performance metrics (AUC, ECE, drift, label shift, etc.) and returns a structured health classification label plus a detailed explanation — helping teams audit deployed clinical models for drift, calibration failure, class imbalance, and other issues.
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Base Model** | [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) |
+| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) via PEFT |
+| **Training Precision** | 8-bit quantized (BitsAndBytesConfig) |
+| **Merged Precision** | FP16 (float16 safetensors) |
+| **Parameters** | ~3.8B |
+| **Model Size** | 7.65 GB (2 safetensor shards) |
+| **LoRA Rank (r)** | 16 |
+| **LoRA Alpha** | 32 |
+| **LoRA Dropout** | 0.05 |
+| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
+| **Task Type** | Causal Language Modeling |
+| **PEFT Version** | 0.18.0 |
+| **Training Epochs** | 3 |
+| **Final Loss** | ~0.41 |
+
+---
+
+## Intended Use
+
+### What this model does
+
+Given a JSON report of clinical ML model performance metrics, the model:
+
+1. Assigns a **Category** label (e.g. `Calibration Failure`, `Major Drift`, `Class Imbalance Problem`, `Healthy`)
+2. Generates a concise **Explanation** with observations and recommendations
+
+### Intended users
+
+- ML engineers monitoring deployed clinical models
+- Healthcare data science teams running periodic model audits
+- Researchers studying automated model health assessment
+
+### Out-of-scope use
+
+- Not suitable for direct clinical decision-making or patient diagnosis
+- Not a replacement for domain expert review of model performance
+- Not designed for non-clinical ML tasks
+- Should not be used on data types outside its training distribution (non-tabular metrics, images, etc.)
+
+---
+
+## How to Use
+
+### Requirements
+
+```bash
+pip install transformers torch accelerate
+```
+
+### Basic inference
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "PhantomAjusshi/phi3-auditor-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",
+    trust_remote_code=True,  # Required for custom Phi-3 modeling files
+)
+
+report = """{
+  "auc": 0.863,
+  "accuracy": 0.83,
+  "precision": 0.79,
+  "recall": 0.69,
+  "f1": 0.79,
+  "ece": 0.278,
+  "brier": 0.263,
+  "drift": 0.03,
+  "missing_rate": 0.003,
+  "label_shift": 0.06,
+  "pos_rate": 0.10,
+  "data_integrity_issues": 0
+}"""
+
+prompt = (
+    f"<|system|>\nYou are a clinical AI auditor model.\n"
+    f"<|user|>\nInstruction: Analyze the clinical model report and classify its health.\n\nReport:\n{report}\n"
+    f"<|assistant|>\n"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=400,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=1.2,
+        do_sample=True,
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Extract only the assistant's reply
+reply = response.split("<|assistant|>")[-1].strip()
+print(reply)
+```
+
+### Expected output format
+
+```
+Category: Calibration Failure
+Explanation: High calibration error (ECE 0.278) despite reasonable discrimination (AUC 0.863).
+The model's probability outputs are poorly aligned with actual outcomes. Recommend
+recalibration using Platt scaling or isotonic regression, and threshold review.
+```
+
+### Input metrics reference
+
+| Metric | Description |
+|---|---|
+| `auc` | Area Under the ROC Curve |
+| `accuracy` | Overall classification accuracy |
+| `precision` | Positive predictive value |
+| `recall` | Sensitivity / true positive rate |
+| `f1` | Harmonic mean of precision and recall |
+| `ece` | Expected Calibration Error |
+| `brier` | Brier score (probabilistic accuracy) |
+| `drift` | Feature distribution drift score |
+| `missing_rate` | Rate of missing input features |
+| `label_shift` | Output label distribution shift |
+| `pos_rate` | Positive prediction rate |
+| `data_integrity_issues` | Count of detected data quality issues |
+
+---
+
+## Training Details
+
+### Dataset
+
+- **Name:** Custom synthetic clinical audit dataset (`audit_dataset_v2_5000.json`)
+- **Size:** 5,000 labeled samples
+- **Split:** 80% train (4,000) / 20% test (1,000)
+- **Format:** JSONL — each record has `instruction`, `input` (metrics JSON), `output` (category + explanation)
+- **Generation date:** November 17, 2025
+
+Each sample pairs a set of synthetic model performance metrics with a human-written audit label and explanation covering categories such as:
+- Healthy / Passing
+- Calibration Failure
+- Major Drift / Potential Drift
+- Class Imbalance Problem
+- Data Integrity Issue
+- Needs Review / Critical Failure
+
+### Training procedure
+
+The base model was loaded in 8-bit using `BitsAndBytesConfig` and adapted with LoRA targeting the attention projection layers (`q_proj`, `k_proj`, `v_proj`, `o_proj`). After training, the LoRA adapter was merged into the base model weights using `peft.merge_and_unload()` and saved as full FP16 safetensors.
+
+**Prompt format used during training:**
+
+```
+<|system|>
+You are an AI auditor analyzing clinical model performance reports.
+<|user|>
+Instruction: Analyze the clinical model report and classify its health.
+
+Report:
+{ ...metrics JSON... }
+<|assistant|>
+Category: <label>
+Explanation: <explanation>
+```
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Epochs | 3 |
+| Batch size | 4 |
+| Gradient accumulation steps | 4 |
+| Effective batch size | 16 |
+| Learning rate | 1e-4 |
+| Warmup ratio | 0.1 |
+| Max sequence length | 512 |
+| Optimizer | AdamW (default) |
+| Precision | FP16 (mixed) |
+
+### Training loss
+
+| Step | Epoch | Loss |
+|---|---|---|
+| 50 | 0.22 | 1.623 |
+| 100 | 0.44 | 0.657 |
+| 150 | 0.67 | 0.444 |
+| 200 | 0.89 | 0.420 |
+| 300 | 1.33 | 0.413 |
+| 450 | 2.00 | 0.412 |
+| 600 | 2.67 | 0.408 |
+| 675 | 3.00 | ~0.410 |
+
+Loss converged rapidly after the first 150 steps, stabilizing around 0.41 for the remainder of training.
+
+---
+
+## Evaluation
+
+The model was evaluated on a held-out test set of 1,000 samples using weighted precision, recall, F1, and accuracy computed by extracting the `Category:` field from generated outputs and comparing to ground-truth labels.
+
+> Formal evaluation metrics will be added here once a full benchmark run is completed.
+
+---
+
+## Limitations & Bias
+
+- **Synthetic training data:** The model was trained entirely on synthetically generated audit reports. Real-world clinical model metrics may follow different distributions or contain edge cases not represented in training.
+- **Label sensitivity:** The model may be sensitive to metric combinations near decision boundaries between categories.
+- **No temporal reasoning:** The model does not reason about metric trends over time — each inference is based on a single snapshot of metrics.
+- **English only:** All training data is in English.
+- **Not a substitute for expert review:** Outputs should be treated as decision-support, not a final audit verdict.
+
+---
+
+## Repository & Related Work
+
+- **Training code:** [Hospital-Audit-Trained-Model (GitHub)](https://github.com/PhantomAjusshi/Hospital-Audit-Trained-Model)
+- **Web application:** [Hospital-Model-Audit-Website (GitHub)](https://github.com/PhantomAjusshi/Hospital-Model-Audit-Website) — a full-stack Next.js + FastAPI interface that uses this model via llama.cpp
+
+---
+
+## Citation
+
+If you use this model in your work, please cite:
+
+```bibtex
+@misc{phi3-auditor-merged,
+  author       = {PhantomAjusshi},
+  title        = {phi3-auditor-merged: Phi-3-mini fine-tuned for clinical AI model auditing},
+  year         = {2025},
+  publisher    = {HuggingFace},
+  url          = {https://huggingface.co/PhantomAjusshi/phi3-auditor-merged}
+}
+```
+
+---
+
+## License
+
+This model is released under the **MIT License**.
+
+The base model ([microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)) is subject to Microsoft's Phi-3 license. Please review it before use in commercial or production settings.

added_tokens.json

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+{
+  "<|assistant|>": 32001,
+  "<|endoftext|>": 32000,
+  "<|end|>": 32007,
+  "<|placeholder1|>": 32002,
+  "<|placeholder2|>": 32003,
+  "<|placeholder3|>": 32004,
+  "<|placeholder4|>": 32005,
+  "<|placeholder5|>": 32008,
+  "<|placeholder6|>": 32009,
+  "<|system|>": 32006,
+  "<|user|>": 32010
+}

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'user' %}{{'<|user|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>
+' + message['content'] + '<|end|>
+'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
+' }}{% else %}{{ eos_token }}{% endif %}

config.json

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+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "float16",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "partial_rotary_factor": 1.0,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 2047,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "vocab_size": 32064
+}

configuration_phi3.py

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+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+
+""" Phi-3 model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
+    "microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
+}
+
+
+class Phi3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
+    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
+    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
+
+    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 32064):
+            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3Model`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            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`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        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 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        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-05):
+            The epsilon value used for the RMSNorm.
+        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`. Whether to tie weight embeddings or not.
+        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*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and 
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size 
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3Model, Phi3Config
+
+    >>> # Initializing a Phi-3 style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_adjustment()
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_adjustment(self):
+        """
+        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
+        """
+        if self.rope_scaling is None:
+            return
+
+        rope_scaling_type = self.rope_scaling.get("type", None)
+
+        # For backward compatibility if previous version used "su" or "yarn"
+        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
+            self.rope_scaling["type"] = "longrope"
+
+    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) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )

generation_config.json

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+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<|placeholder1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<|placeholder2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<|placeholder3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<|placeholder4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<|end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<|placeholder5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<|placeholder6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 4096,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}