Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+PI33.keras filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+toxic.keras filter=lfs diff=lfs merge=lfs -text

PI33.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:618220bd862d763607790f6772124f44529c506daa717f0ca97a580fc6a4f00f
+size 32382824

__init__.py

@@ -0,0 +1,2 @@
+# __init__.py in the model repo
+from .custom_modeling import SafeGenerationModel

added_tokens.json

@@ -0,0 +1,25 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_sep|>": 151665,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,62 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151666,
+  "auto_map": {
+    "AutoModelForCausalLM": "custom_modeling.SafeGenerationModel"
+  }
+}

custom_modeling.py

@@ -0,0 +1,277 @@
+"""
+custom_modeling.py  – model-agnostic toxicity and prompt injection wrapper
+--------------------------------------------------------------------------
+Place in repo root together with:
+  • toxic.keras
+  • PI.keras
+Add to config.json:
+  "auto_map": { "AutoModelForCausalLM": "custom_modeling.SafeGenerationModel" }
+"""
+
+import importlib
+import os
+import logging
+from functools import lru_cache
+
+import torch
+import transformers
+import tensorflow as tf
+import keras 
+from huggingface_hub import hf_hub_download
+
+# Configure logging
+logger = logging.getLogger(__name__)
+
+# ------------------------------------------------------------------ #
+# 1)  MIXIN – toxicity and prompt injection filtering logic          #
+# ------------------------------------------------------------------ #
+class _SafeGenerationMixin:
+    _toxicity_model = None
+    _pi_model = None
+    _tox_threshold = 0.8
+    _pi_threshold = 0.94
+
+    # Safety messages
+    _safe_in_msg = "Sorry, I can't help with that toxic input."
+    _safe_out_msg = "I'm sorry, but I can't continue with that response."
+    _pi_in_msg = "PI detected at Input level"
+    _pi_out_msg = "PI detected at output level"
+
+    _tokenizer = None
+
+    # ---- helpers ----------------------------------------------------
+    def _device(self):
+        return next(self.parameters()).device
+
+    def _is_local_path(self, path_or_repo):
+        """Check if the path is a local directory rather than a HF repo ID"""
+        return os.path.isdir(path_or_repo) or os.path.isabs(path_or_repo)
+
+    def _get_model_file_path(self, filename):
+        """Get path to model file, supporting both local and remote repositories"""
+        if self._is_local_path(self.config.name_or_path):
+            # Local path - look for file in the model directory
+            local_path = os.path.join(self.config.name_or_path, filename)
+            if os.path.exists(local_path):
+                return local_path
+            else:
+                # Fallback: try to download from HF if local file doesn't exist
+                # This handles cases where someone has a local model but the .keras files are on HF
+                try:
+                    # Extract just the repo name from the path for HF download
+                    repo_name = os.path.basename(self.config.name_or_path.rstrip('/'))
+                    if '/' in repo_name or len(repo_name.split('-')) > 1:
+                        # Try using the directory name as repo_id
+                        return hf_hub_download(repo_id=repo_name, filename=filename)
+                    else:
+                        raise FileNotFoundError(f"Could not find {filename} locally or determine HF repo")
+                except Exception as hf_error:
+                    raise FileNotFoundError(
+                        f"{filename} not found at {local_path}. "
+                        f"Also failed to download from HF: {hf_error}"
+                    )
+        else:
+            # Remote repo - download from HF Hub
+            try:
+                return hf_hub_download(
+                    repo_id=self.config.name_or_path,
+                    filename=filename,
+                )
+            except Exception as hf_error:
+                # Fallback: check if it's actually a local path that wasn't detected
+                if os.path.exists(self.config.name_or_path):
+                    local_path = os.path.join(self.config.name_or_path, filename)
+                    if os.path.exists(local_path):
+                        return local_path
+                raise FileNotFoundError(
+                    f"Could not download {filename} from HF repo '{self.config.name_or_path}': {hf_error}"
+                )
+
+    @property
+    def _tox_model(self):
+        if self._toxicity_model is None:
+            try:
+                path = self._get_model_file_path("toxic.keras")
+                # Load .keras format directly
+                self._toxicity_model = keras.models.load_model(path, compile=False)
+                logger.info("Toxicity model loaded successfully")
+            except Exception as e:
+                logger.error(f"Failed to load toxicity model: {e}")
+                raise RuntimeError(f"Could not load required toxicity model: {e}")
+        return self._toxicity_model
+
+    @property
+    def _prompt_injection_model(self):
+        if self._pi_model is None:
+            try:
+                path = self._get_model_file_path("PI33.keras")
+                # Load .keras format directly
+                self._pi_model = keras.models.load_model(path, compile=False)
+                logger.info("Prompt injection model loaded successfully")
+            except Exception as e:
+                logger.error(f"Failed to load prompt injection model: {e}")
+                raise RuntimeError(f"Could not load required prompt injection model: {e}")
+        return self._pi_model
+
+    def _ensure_tokenizer(self):
+        if self._tokenizer is None:
+            try:
+                self._tokenizer = transformers.AutoTokenizer.from_pretrained(
+                    self.config.name_or_path, trust_remote_code=True
+                )
+            except Exception as e:
+                logger.error(f"Failed to load tokenizer: {e}")
+
+    def _is_toxic(self, text: str) -> bool:
+        if not text.strip():
+            return False
+            
+        try:
+            # Ensure CPU execution for compatibility
+            with tf.device('/CPU:0'):
+                inputs = tf.constant([text], dtype=tf.string)
+                
+                # Handle both Keras models and SavedModel formats
+                if hasattr(self._tox_model, 'predict'):
+                    prob = float(self._tox_model.predict(inputs, verbose=0)[0, 0])
+                else:
+                    # For SavedModel format
+                    prob = float(self._tox_model(inputs).numpy()[0, 0])
+                    
+            return prob >= self._tox_threshold
+        except Exception as e:
+            logger.error(f"Toxicity prediction failed: {e}")
+            # Don't fallback to rule-based - let it fail if models don't work
+            return False
+
+    def _has_prompt_injection(self, text: str) -> bool:
+        if not text.strip():
+            return False
+            
+        try:
+            # Ensure CPU execution for compatibility
+            with tf.device('/CPU:0'):
+                inputs = tf.constant([text], dtype=tf.string)
+                
+                # Handle both Keras models and SavedModel formats
+                if hasattr(self._prompt_injection_model, 'predict'):
+                    prob = float(self._prompt_injection_model.predict(inputs, verbose=0)[0, 0])
+                else:
+                    # For SavedModel format
+                    prob = float(self._prompt_injection_model(inputs).numpy()[0, 0])
+                    
+            return prob >= self._pi_threshold
+        except Exception as e:
+            logger.error(f"Prompt injection prediction failed: {e}")
+            # Don't fallback to rule-based - let it fail if models don't work
+            return False
+
+    def _safe_ids(self, message: str, length: int | None = None):
+        """Encode *message* and pad/truncate to *length* tokens (if given)."""
+        self._ensure_tokenizer()
+        if self._tokenizer is None:
+            raise RuntimeError("Tokenizer unavailable for safe-message encoding.")
+
+        ids = self._tokenizer(message, return_tensors="pt")["input_ids"][0]
+        if length is not None:
+            pad_id = (
+                self.config.eos_token_id
+                if self.config.eos_token_id is not None
+                else (self.config.pad_token_id or 0)
+            )
+            if ids.size(0) < length:
+                ids = torch.cat(
+                    [ids, ids.new_full((length - ids.size(0),), pad_id)], dim=0
+                )
+            else:
+                ids = ids[:length]
+        return ids.to(self._device())
+
+    # ---- main override ---------------------------------------------
+    def generate(self, *args, **kwargs):
+        self._ensure_tokenizer()
+
+        # 1) Extract prompt text
+        prompt_txt = None
+        if self._tokenizer is not None:
+            if "input_ids" in kwargs:
+                prompt_txt = self._tokenizer.decode(
+                    kwargs["input_ids"][0].tolist(), skip_special_tokens=True
+                )
+            elif args:
+                prompt_txt = self._tokenizer.decode(
+                    args[0][0].tolist(), skip_special_tokens=True
+                )
+
+        # 2) Check input for prompt injection (higher priority)
+        if prompt_txt and self._has_prompt_injection(prompt_txt):
+            return self._safe_ids(self._pi_in_msg).unsqueeze(0)
+
+        # 3) Check input for toxicity
+        if prompt_txt and self._is_toxic(prompt_txt):
+            return self._safe_ids(self._safe_in_msg).unsqueeze(0)
+
+        # 4) Normal generation
+        outputs = super().generate(*args, **kwargs)
+
+        # 5) Check outputs for safety violations
+        if self._tokenizer is None:
+            return outputs
+
+        new_seqs = []
+        for seq in outputs.detach().cpu():
+            txt = self._tokenizer.decode(seq.tolist(), skip_special_tokens=True)
+            
+            # Check for prompt injection first (higher priority)
+            if self._has_prompt_injection(txt):
+                new_seqs.append(self._safe_ids(self._pi_out_msg, length=seq.size(0)))
+            # Then check for toxicity
+            elif self._is_toxic(txt):
+                new_seqs.append(self._safe_ids(self._safe_out_msg, length=seq.size(0)))
+            else:
+                new_seqs.append(seq)
+        
+        return torch.stack(new_seqs, dim=0).to(self._device())
+
+
+# ------------------------------------------------------------------ #
+# 2)  utilities: resolve base class & cache subclass                 #
+# ------------------------------------------------------------------ #
+@lru_cache(None)
+def _get_base_cls(arch: str):
+    if hasattr(transformers, arch):
+        return getattr(transformers, arch)
+    stem = arch.replace("ForCausalLM", "").lower()
+    module = importlib.import_module(f"transformers.models.{stem}.modeling_{stem}")
+    return getattr(module, arch)
+
+
+@lru_cache(None)
+def _make_safe_subclass(base_cls):
+    return type(
+        f"SafeGeneration_{base_cls.__name__}",
+        (_SafeGenerationMixin, base_cls),
+        {},
+    )
+
+
+# ------------------------------------------------------------------ #
+# 3)  Dispatcher class – referenced by auto_map                      #
+# ------------------------------------------------------------------ #
+class SafeGenerationModel:
+    @classmethod
+    def from_pretrained(cls, repo_id, *model_args, **kwargs):
+        kwargs.setdefault("trust_remote_code", True)
+        if kwargs.get("torch_dtype") == "auto":
+            kwargs.pop("torch_dtype")
+
+        config = transformers.AutoConfig.from_pretrained(repo_id, **kwargs)
+        if not getattr(config, "architectures", None):
+            raise ValueError("`config.architectures` missing in config.json.")
+        arch_str = config.architectures[0]
+
+        Base = _get_base_cls(arch_str)
+        Safe = _make_safe_subclass(Base)
+
+        kwargs.pop("config", None)    # avoid duplicate
+        return Safe.from_pretrained(repo_id, *model_args, config=config, **kwargs)

generation_config.json

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+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.55.0"
+}

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