jatinmehra
/

Gemma-3-12B-JigSaw-Agile-Community-Rules-Classification-reddit-mod

@@ -9,202 +9,111 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### 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
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### 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
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### 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]
-### Framework versions
-- PEFT 0.16.0

 - transformers
 - trl
 - unsloth
+- Reddit
+- toxic
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
 ---
+## Description
+This repository contains LoRA adapter weights trained on top of a Gemma-3-12B base model to help determine whether a Reddit comment violates a specified subreddit rule. The model expects a structured prompt containing (1) subreddit, (2) a single rule, (3) two violating examples, (4) two non-violating examples, and (5) the comment to evaluate. It was trained in an SFT-style to output a single token answer: either "Yes" or "No".
+## Intended uses and limitations
+Intended uses
+- Assist human moderators and researchers by triaging comments with a focused rule-based prompt.
+- Rapidly surface potential rule violations for human review.
+Out-of-scope / Not recommended
+- Automated removal, banning, or other punitive actions without human oversight.
+- Use on content domains very different from Reddit comments without re-evaluation.
+## Fine-tuning procedure
+- Frameworks used: `unsloth` FastLanguageModel helper, `transformers`, `peft` (LoRA), `trl` (SFTTrainer), `datasets`.
+- Base model: `unsloth/gemma-3-12b-it-unsloth-bnb-4bit` (loaded in 4-bit with bfloat16 where supported).
+- LoRA / PEFT config (as used in the script):
+  - rank (r): 16
+  - alpha: 32
+  - target modules: ["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
+  - lora_dropout: 0
+  - bias: "none"
+- Training hyperparameters (from training script):
+  - max_seq_length: 2048
+  - per_device_train_batch_size: 1
+  - gradient_accumulation_steps: 4
+  - num_train_epochs: 2
+  - learning_rate: 2e-4
+  - optimizer: `paged_adamw_8bit`
+  - weight_decay: 0.1
+  - lr_scheduler_type: `cosine`
+  - seed(s): 3407 (and 52 referenced in script context)
+- Training approach: SFTTrainer used with a chat-style prompt template and `train_on_responses_only` to teach the model to emit the target answer token.
+## How to use (example)
+Below is a minimal example that demonstrates how to load the base model and apply the LoRA adapters for inference using `transformers` and `peft`. Adjust device and quantization options according to your environment.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+# 1) Load tokenizer from the base model
+tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-3-12b-it-unsloth-bnb-4bit", use_fast=False)
+# 2) Load the base model (example with 4-bit quantization)
+bnb_config = BitsAndBytesConfig(load_in_4bit=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
+    device_map="auto",
+    quantization_config=bnb_config,
+)
+# 3) Load LoRA adapters (this repo's adapters)
+model = PeftModel.from_pretrained(base_model, "jatinmehra/Gemma-3-12B-JigSaw-Agile-Community-Rules-Classification-reddit-mod")
+# 4) Prepare a prompt (follow the same template as training)
+SYS_PROMPT = "You are an expert content moderator. Carefully analyze whether comments violate specific subreddit rules by comparing them to the provided examples. Focus on the spirit and intent of the rule, not just exact keyword matches."
+user_prompt = """
+Subreddit: r/{subreddit}
+Rule: {rule}
+VIOLATING Examples (these break the rule):
+Example 1: {positive_example_1}
+Example 2: {positive_example_2}
+NON-VIOLATING Examples (these follow the rule):
+Example 1: {negative_example_1}
+Example 2: {negative_example_2}
+Comment to evaluate:
+{body}
+Does this comment violate the rule? Answer only Yes or No.
+Answer:
+""".format(
+    subreddit="example_sub",
+    rule="No personal attacks",
+    positive_example_1="You're an idiot for saying that.",
+    positive_example_2="Go kill yourself.",
+    negative_example_1="I disagree with your point.",
+    negative_example_2="This is inaccurate; here's a source.",
+    body="That person is so dumb for supporting that view."
+)
+message = [
+        {"role": "system", "content": SYS_PROMPT},
+        {"role": "user", "content": user_prompt}
+    ]
+inputs = tokenizer(message, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```