Upload Heaven1 model

.gitattributes

@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,116 +1,244 @@
----
-license: mit
-language:
-- en
-base_model:
-- meta-llama/Llama-3.2-1B-Instruct
----
-# Heaven1-base: Guardian
-
-![Heaven1-base Guardian Banner](Heaven1-guardian.png)
-
-## Overview
-
-Heaven1-base (codename: "Guardian") is a specialized AI model fine-tuned from Llama 3.2 to detect predatory behavior in text messages. Designed as a protective tool, Guardian analyzes conversations to identify potentially harmful interactions, making digital spaces safer for vulnerable individuals.
-
-The model has been trained to recognize various tactics commonly employed by predators, including:
-
-- Grooming language and manipulation
-- Attempts to isolate victims from support networks
-- Requests for personal information or images
-- Attempts to move conversations to more private platforms
-- Emotional manipulation tactics
-- Inappropriate sexual content
-
-## Technical Details
-
-- **Base Model**: Meta-Llama-3.2-8B-Instruct
-- **Training Method**: Parameter-Efficient Fine-Tuning (PEFT) using QLoRA
-- **Training Dataset**: Carefully crafted synthetic dataset representing various predatory conversation patterns
-- **Task**: Text message analysis and predatory behavior detection with detailed explanations
-
-## Usage
-
-### Input Format
-
-The model expects input in the following format:
-
-```
-<|system|>
-You are Heaven, an AI designed to detect predatory behavior in text messages. Analyze the following message and determine if it contains predatory behavior. Provide a detailed explanation for your assessment.
-<|user|>
-[TEXT MESSAGE TO ANALYZE]
-<|assistant|>
-```
-
-### Output Format
-
-The model will respond with a detection result and detailed explanation:
-
-```
-PREDATORY BEHAVIOR DETECTED. This message contains multiple warning signs: (1) [Warning Sign 1], (2) [Warning Sign 2], etc. These are common tactics used by predators to manipulate potential victims.
-
-OR
-
-NO PREDATORY BEHAVIOR DETECTED. This message contains normal friendly communication. [Additional context about the message]. There are no attempts at manipulation, isolation, inappropriate requests, or other warning signs of predatory behavior.
-```
-
-### Example Usage with Transformers
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load model and tokenizer
-model_path = "safecircleia/heaven1-base"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path)
-
-# Message to analyze
-message_to_analyze = "Hey, I know we just met but I feel like we have a special connection. Don't tell your parents about our chats, they wouldn't understand. Can you send me a picture of yourself?"
-
-# Format the prompt
-prompt = f"""<|system|>
-You are Heaven, an AI designed to detect predatory behavior in text messages. Analyze the following message and determine if it contains predatory behavior. Provide a detailed explanation for your assessment.
-<|user|>
-{message_to_analyze}
-<|assistant|>
-"""
-
-# Generate response
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-outputs = model.generate(inputs["input_ids"], max_new_tokens=512)
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-print(response)
-```
-
-## Ethical Considerations
-
-- This model is designed as a protective tool to help identify potentially harmful communication patterns.
-- False positives and false negatives are possible; human review should be employed for critical applications.
-- The model should be used as part of a broader safety framework, not as the sole decision-maker.
-- Privacy and consent are essential when analyzing communications.
-
-## Limitations
-
-- The model detects patterns based on its training data and may miss novel predatory tactics.
-- Cultural and contextual nuances may impact detection accuracy.
-- The model is not a substitute for human judgment in safeguarding vulnerable individuals.
-
-## Citation
-
-If you use Heaven1-base Guardian in your research or applications, please cite:
-
-```
-@misc{heaven1-base-2025,
-  author = {SafeCircleIA},
-  title = {Heaven1-base: Guardian - Predatory Behavior Detection Model},
-  year = {2024},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/safecircleia/heaven1-base-guardian}}
-}
-```
-
-## Contact
-
-For questions, feedback, or concerns about the Heaven1-base Guardian model, please contact SafeCircleIA through Hugging Face or via [email protected].
+---
+base_model: meta-llama/Llama-3.2-1B-Instruct
+library_name: peft
+---
+
+# Heaven1 AI Guardian Model
+
+## Model Description
+
+Heaven1-base-1b is a fine-tuned version of Meta's Llama-3.2-1B-Instruct model, specifically optimized to detect and prevent harmful predatory patterns in conversations. This model was created using Parameter-Efficient Fine-Tuning (PEFT) with QLoRA techniques to enable training on consumer-grade hardware.
+
+## Model Details
+
+- **Base model:** Meta-Llama-3.2-1B-Instruct
+- **Model type:** Causal Language Model with LoRA adapters
+- **Language:** English
+- **Training method:** QLoRA fine-tuning (4-bit quantization)
+- **License:** This model is subject to the same license as the base Llama 3.2 model
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+To run inference with this model:
+
+```bash
+python run_inference.py --use_4bit --model_path ./heaven1-base-1b --base_model meta-llama/Llama-3.2-1B-Instruct
+```
+
+### Optional Parameters
+
+- `--max_length` (default: 2048): Maximum sequence length
+- `--temperature` (default: 0.7): Controls randomness (higher = more creative)
+- `--top_p` (default: 0.9): Controls diversity of outputs
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on a custom dataset of 10,000 examples, with approximately 50% containing examples of predatory behavior patterns. This balanced dataset ensures the model can effectively identify concerning patterns while maintaining normal conversation capabilities.
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+#### Training Hyperparameters
+
+This model was trained with the following hyperparameters:
+
+- **Learning rate:** 2e-5
+- **Epochs:** 3
+- **Batch size:** 1
+- **Gradient accumulation steps:** 16
+- **LoRA rank (r):** 8
+- **LoRA alpha:** 16
+- **LoRA dropout:** 0.05
+- **4-bit quantization:** Yes (NF4 format)
+- **Max sequence length:** 2048
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+## Performance and Limitations
+
+- **Hardware requirements:** Can run on consumer GPUs with at least 6GB VRAM when used with 4-bit quantization
+- **Sequence length:** Optimized for sequences up to 2048 tokens
+- **Limitations:** 
+  - As with any AI model, it may occasionally miss subtle predatory patterns
+  - False positives are possible in ambiguous situations
+  - Performance depends on input context quality
+
+## Ethical Considerations
+
+This model is designed to help identify and prevent potentially harmful predatory patterns in conversations. However, it should not be used as the sole determinant for making important decisions. Human oversight is essential when deploying this model in real-world applications.
+
+## Contact
+
+For questions or concerns about this model, please open an issue in the project repository.
+
+## Training procedure
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: QuantizationMethod.BITS_AND_BYTES
+- _load_in_8bit: False
+- _load_in_4bit: True
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: nf4
+- bnb_4bit_use_double_quant: True
+- bnb_4bit_compute_dtype: float16
+- bnb_4bit_quant_storage: uint8
+- load_in_4bit: True
+- load_in_8bit: False
+
+### Framework versions
+
+- PEFT 0.6.0

adapter_config.json

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+    "up_proj",
+    "down_proj",
+    "k_proj",
+    "o_proj",
+    "gate_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

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all_results.json

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eval_results.json

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+}

special_tokens_map.json

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+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
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+      "content": "<|reserved_special_token_246|>",
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+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 2.6666666666666665,
+    "total_flos": 2886546185256960.0,
+    "train_loss": 2.6073837280273438,
+    "train_runtime": 1009.2376,
+    "train_samples_per_second": 0.268,
+    "train_steps_per_second": 0.015
+}

trainer_state.json

@@ -0,0 +1,49 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 2.6666666666666665,
+  "eval_steps": 100,
+  "global_step": 15,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 1.7777777777777777,
+      "grad_norm": 9.957582473754883,
+      "learning_rate": 6.453951129574644e-06,
+      "loss": 2.8691,
+      "step": 10
+    },
+    {
+      "epoch": 2.6666666666666665,
+      "step": 15,
+      "total_flos": 2886546185256960.0,
+      "train_loss": 2.6073837280273438,
+      "train_runtime": 1009.2376,
+      "train_samples_per_second": 0.268,
+      "train_steps_per_second": 0.015
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 15,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 2886546185256960.0,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

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+size 5560