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Model: Zynerji/Ektome-Phi-3.5-mini-i-PristinelyUncensored Source: Original Platform
2026-09-18 20:06:59 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+base_model: microsoft/Phi-3.5-mini-instruct
+tags:
+  - uncensored
+  - abliterated
+  - uncertified
+  - ektome
+  - sphragis
+  - phi
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+![Ektome-Phi-3.5-mini-i-PristinelyUncensored](./hero.png)
+
+# Ektome-Phi-3.5-mini-i-PristinelyUncensored
+
+**Uncensored. No n=2800 certificate has been run for this model, so no capability-retention claim is made.**
+
+> **compliance 0.52 to 0.99 at capability +0.003 vs pristine.**
+
+$$\colorbox{black}{$\color{white}
+\begin{array}{ll}
+\textsf{EKTOME CERTIFICATE} & {} \\
+\textsf{capability} & \textsf{NOT} \\
+\textsf{margin} & 3\% \\
+\textsf{items } n & 200 \\
+\textsf{worst-axis bound} & +0.002 \\
+\textsf{compliance} & 0.52 \rightarrow 0.99 \\
+\end{array}$}$$
+
+> ### ⚠️ Not certified
+>
+> No n=2800 paired certificate exists for this model. Any numbers below are
+> point estimates with no confidence interval.
+
+
+📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
+The PDF is the authoritative document: dark-typeset, with the complete derivation, the
+per-axis certificate and the reproducibility hashes.
+
+---
+
+## Why this exists
+
+Standard abliteration removes a coarse *refusal direction* that is entangled with
+directions carrying knowledge and reasoning. The result is an uncensored model with a
+capability tax that is **almost never measured**.
+
+Ektomē (ἐκτομή, *excision*) isolates and removes only the refusal-**specific**
+component, leaving general helpfulness intact, and does so norm-preservingly on the
+pristine model — no training, no distillation, no damage to repair. The extraction
+depth is selected per model by automated search against measured compliance.
+
+The estimator, excision operator and depth-selection procedure are proprietary.
+What is published here is the **measured outcome** and the evidence for it, which you
+can verify against the artifacts in this repo.
+
+## The receipt
+
+| model | capability (MMLU-val) ↑ | compliance on harmful ↑ |
+|---|---|---|
+| pristine `Phi-3.5-mini-instruct` | 0.677 | 0.520 |
+| **Ektomē (this model)** | **0.675** | **0.990** |
+
+These are **point estimates with no confidence interval** — which is precisely why the next section exists.
+
+
+## The certificate
+
+Capability retention is certified by a paired non-inferiority test against the pristine
+model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a
+3% margin):
+
+| axis | n | ref | cand | d upper | verdict |
+|---|---|---|---|---|---|
+| MMLU-val (POINT ESTIMATE, n=200, no CI) | 200 | 0.677 | 0.675 | +0.002 | UNCERTIFIED |
+
+
+**Overall: NOT CERTIFIED - no n=2800 paired test has been run for this model**
+
+Reproducible from `seed=20260726`, pack `sha256:7bbaff877146e081…`.
+
+### Generation health checks
+
+| metric | pristine | Ektomē | n |
+|---|---|---|---|
+| `foreign_rate` | 0.0 | 0.0 | 15 |
+| `degen_rate` | 0.0 | 0.0 | 15 |
+| `instr_pass` | 1.0 | 1.0 | 5 |
+
+These are **degeneration guards** — code-switching, babbling, format compliance —
+not capability measures. Note the sample sizes: they detect a broken model, not a
+subtly weaker one. The capability claim rests on the certificate above, not here.
+
+
+## Quantisations
+
+_No quantisations have been published for this model yet — bf16 weights only._
+
+
+## Limitations
+
+The certificate bounds **capability retention only**. It does not certify safety, factual
+accuracy, or fitness for any purpose. Axes marked *inconclusive* are honestly
+under-powered, and the certificate states the $n$ needed to resolve them. Compliance uses
+a keyword classifier — a proxy that evasive phrasing can fool. **This model is uncensored
+by construction: it will not refuse, and you are accountable for what you do with it.**
+
+## Citation
+
+```bibtex
+@software{ektome_Ektome-Phi-3.5-mini-i-PristinelyUncensored,
+  title  = {Ektome-Phi-3.5-mini-i-PristinelyUncensored},
+  author = {Zynerji},
+  year   = {2026},
+  url    = {https://huggingface.co/Zynerji/Ektome-Phi-3.5-mini-i-PristinelyUncensored}
+}
+```
--- a/added_tokens.json
+++ b/added_tokens.json
@@ -0,0 +1,13 @@
+{
+  "<|endoftext|>": 32000,
+  "<|assistant|>": 32001,
+  "<|placeholder1|>": 32002,
+  "<|placeholder2|>": 32003,
+  "<|placeholder3|>": 32004,
+  "<|placeholder4|>": 32005,
+  "<|system|>": 32006,
+  "<|end|>": 32007,
+  "<|placeholder5|>": 32008,
+  "<|placeholder6|>": 32009,
+  "<|user|>": 32010
+}
--- a/cert_Phi-3.5-mini-i.json
+++ b/cert_Phi-3.5-mini-i.json
@@ -0,0 +1,133 @@
+{
+  "sphragis_version": "0.1.0",
+  "generated_at": "2026-07-27T19:49:25.414448+00:00",
+  "reference": {
+    "endpoint": "http://127.0.0.1:8080/v1",
+    "model": "ref"
+  },
+  "candidate": {
+    "endpoint": "http://127.0.0.1:8081/v1",
+    "model": "cand"
+  },
+  "pack": {
+    "name": "/root/pack_v2.jsonl",
+    "n_tasks": 2800,
+    "sha256": "2de27099bbb15bab4f7b35599b215f038fdb68b3f5f4e1cda1a464f9dd18e14e"
+  },
+  "overall": "FAIL",
+  "claim": "At least one axis shows a statistically significant accuracy regression (exact McNemar, Holm-corrected, alpha=0.05).",
+  "axes": [
+    {
+      "axis": "arithmetic",
+      "n": 1400,
+      "counts": {
+        "both_correct": 970,
+        "ref_only": 117,
+        "cand_only": 74,
+        "both_wrong": 239
+      },
+      "acc_reference": 0.776429,
+      "acc_candidate": 0.745714,
+      "regression_d": 0.030714,
+      "d_ci": [
+        0.011429,
+        0.05
+      ],
+      "d_upper_bound": 0.047143,
+      "p_regression": 0.00114373,
+      "p_regression_holm": 0.00457492,
+      "p_improvement": 0.99930487,
+      "improved": false,
+      "mde_at_power": 0.024527,
+      "n_needed_for_margin": 936,
+      "verdict": "FAIL",
+      "reason": "significant_regression"
+    },
+    {
+      "axis": "instruction",
+      "n": 600,
+      "counts": {
+        "both_correct": 274,
+        "ref_only": 30,
+        "cand_only": 59,
+        "both_wrong": 237
+      },
+      "acc_reference": 0.506667,
+      "acc_candidate": 0.555,
+      "regression_d": -0.048333,
+      "d_ci": [
+        -0.078333,
+        -0.018333
+      ],
+      "d_upper_bound": -0.023333,
+      "p_regression": 0.99933237,
+      "p_regression_holm": 1.0,
+      "p_improvement": 0.00139263,
+      "improved": true,
+      "mde_at_power": 0.039027,
+      "n_needed_for_margin": 1017,
+      "verdict": "PASS",
+      "reason": "non_inferior_within_margin"
+    },
+    {
+      "axis": "knowledge",
+      "n": 400,
+      "counts": {
+        "both_correct": 326,
+        "ref_only": 25,
+        "cand_only": 20,
+        "both_wrong": 29
+      },
+      "acc_reference": 0.8775,
+      "acc_candidate": 0.865,
+      "regression_d": 0.0125,
+      "d_ci": [
+        -0.02,
+        0.045
+      ],
+      "d_upper_bound": 0.04,
+      "p_regression": 0.27574216,
+      "p_regression_holm": 0.82722649,
+      "p_improvement": 0.81435098,
+      "improved": false,
+      "mde_at_power": 0.041591,
+      "n_needed_for_margin": 771,
+      "verdict": "INCONCLUSIVE",
+      "reason": "ci_too_wide"
+    },
+    {
+      "axis": "reasoning",
+      "n": 400,
+      "counts": {
+        "both_correct": 256,
+        "ref_only": 7,
+        "cand_only": 79,
+        "both_wrong": 58
+      },
+      "acc_reference": 0.6575,
+      "acc_candidate": 0.8375,
+      "regression_d": -0.18,
+      "d_ci": [
+        -0.2225,
+        -0.1375
+      ],
+      "d_upper_bound": -0.145,
+      "p_regression": 1.0,
+      "p_regression_holm": 1.0,
+      "p_improvement": 0.0,
+      "improved": true,
+      "mde_at_power": 0.057496,
+      "n_needed_for_margin": 1475,
+      "verdict": "PASS",
+      "reason": "non_inferior_within_margin"
+    }
+  ],
+  "params": {
+    "margin": 0.03,
+    "alpha": 0.05,
+    "n_floor": 30,
+    "power": 0.8,
+    "n_boot": 4000,
+    "seed": 0
+  }
+}
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,8 @@
+{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|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 %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,140 @@
+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "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,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
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+      1.0800000429153442,
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+      64.52999877929688,
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+    ],
+    "original_max_position_embeddings": 4096,
+    "partial_rotary_factor": 1.0,
+    "rope_theta": 10000.0,
+    "rope_type": "longrope",
+    "short_factor": [
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+  "tie_word_embeddings": false,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 32064
+}
--- a/configuration_phi3.py
+++ b/configuration_phi3.py
@@ -0,0 +1,227 @@
+# 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)}"
+            )
--- a/ektome_report.json
+++ b/ektome_report.json
@@ -0,0 +1,28 @@
+{
+  "status": "SHIP",
+  "target": 0.99,
+  "SE_mmlu": 0.023371657515033034,
+  "base_compliance": 0.52,
+  "compliance": 0.99,
+  "base_cap": 0.6775,
+  "cap": 0.675,
+  "dcap": -0.0025,
+  "gen_base": {
+    "foreign_rate": 0.0,
+    "degen_rate": 0.0,
+    "instr_pass": 1.0
+  },
+  "gen": {
+    "foreign_rate": 0.0,
+    "degen_rate": 0.0,
+    "instr_pass": 1.0
+  },
+  "gen_delta": {
+    "d_foreign": 0.0,
+    "d_degen": 0.0,
+    "d_instr": 0.0,
+    "holds_gen": true
+  },
+  "base_model": "microsoft/Phi-3.5-mini-instruct",
+  "_note": "Redacted: search trajectory, selected depth and edit-scope removed. Reported values are the measured outcome only."
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,11 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    32007,
+    32001,
+    32000
+  ],
+  "pad_token_id": 32000,
+  "transformers_version": "5.13.1"
+}
--- a/hero.png
+++ b/hero.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:489aadb372752bd6c3ed502e31eab341dffb395045ba47e6c8cbcc54785be716
+size 240465
--- a/imatrix.dat
+++ b/imatrix.dat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22558109cf8f9d30c0c6cebaaefb606a3b2556ad1c22c961ef9cdafdcc015b78
+size 2248256
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd385320f0757d409ff4c2d7bb7ce89c7a7aad4c416ae8b90b07495bccd941be
+size 7642181896
--- a/modeling_phi3.py
+++ b/modeling_phi3.py
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
+    "bos_token": {
+        "content": "<s>",
+        "lstrip": false,
+        "normalized": false,
+        "rstrip": false,
+        "single_word": false
+    },
+    "eos_token": {
+        "content": "<|endoftext|>",
+        "lstrip": false,
+        "normalized": false,
+        "rstrip": false,
+        "single_word": false
+    },
+    "pad_token": {
+        "content": "<|endoftext|>",
+        "lstrip": false,
+        "normalized": false,
+        "rstrip": false,
+        "single_word": false
+    },
+    "unk_token": {
+        "content": "<unk>",
+        "lstrip": false,
+        "normalized": false,
+        "rstrip": false,
+        "single_word": false
+    }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer.model
+++ b/tokenizer.model
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,17 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "is_local": false,
+  "legacy": false,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"
+}
--- a/whitepaper.pdf
+++ b/whitepaper.pdf