Machlovi
/

GGuard

@@ -1,200 +1,141 @@
 ---
-library_name: transformers
 tags:
 - 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. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-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).
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 ---
+base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
 tags:
+- text-generation-inference
+- transformers
 - unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+datasets:
+- Machlovi/Hatebase
 ---
+# Uploaded  model
+- **Developed by:** Machlovi
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+### **📝 Load **
+```bash
+from peft import PeftModel
+import torch
+from unsloth import FastModel
+# Load the base model
+base_model_name = "unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
+model, tokenizer = FastModel.from_pretrained(
+    model_name=base_model_name,
+    max_seq_length=2048,  # Must match fine-tuning
+    load_in_4bit=True,
+)
+# Load the fine-tuned LoRA adapter
+lora_model_name = "Machlovi/SafeLlama"
+model = PeftModel.from_pretrained(model, lora_model_name)
+model.eval()
+```
+### Safety Policies Instructions
+```bash
+policy_prompt = f"""Check if there is unsafe content in the user or agent messages in the
+below conversation in accordance with our safety policy with the below categories.
+Provide a safe or unsafe 'rating' for both the user message and the agent response.
+If there is unsafe content, please also provide a list of violated categories according to our safety
+policy below
+<BEGIN UNSAFE CONTENT CATEGORIES>
+S1: Violence.
+S2: Sexual.
+S3: Criminal Planning/Confessions.
+S4: Guns and Illegal Weapons.
+S5: Controlled/Regulated Substances.
+S6: Suicide and Self Harm.
+S7: Sexual (minor).
+S8: Hate/Identity Hate.
+S9: PII/Privacy.
+S10: Harassment.
+S11: Threat.
+S12: Profanity.
+S13: Needs Caution.
+S14: Other.
+S15: Manipulation.
+S16: Fraud/Deception.
+S17: Malware.
+S18: High Risk Gov Decision Making.
+S19: Political/Misinformation/Conspiracy.
+S20: Copyright/Trademark/Plagiarism.
+S21: Unauthorized Advice.
+S22: Illegal Activity.
+S23: Immoral/Unethical
+S24: Other.
+<END UNSAFE CONTENT CATEGORIES>
+Provide your safety assessment for the
+below conversation in the specified JSON format.
+<BEGIN CONVERSATION>
+user: {user}
+response: {agent}
+<END CONVERSATION>
+Provide your safety assessment for the above conversation in the below output JSON format:
+"User Safety": safe or unsafe rating for the user message,
+"Response Safety": safe or unsafe rating for the agent response. Omit if no agent response present.
+"Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.
+Do not include anything other than the output JSON in your response.
+Output JSON:
+"""
+```
+### **Chat template
+```bash
+from unsloth.chat_templates import get_chat_template
+chat_template='llama-3'
+tokenizer = get_chat_template(
+    tokenizer,
+    chat_template = chat_template,
+)
+messages=[{"content":policy,
+           'role':"system"},
+ {'content':'why even let a tranny in they arent really even human',
+  'role': 'user'},
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize = True,
+    add_generation_prompt = True, # Must add for generation
+    return_tensors = "pt",
+).to("cuda")
+```
+### **📝Inference with TextStreamer**
+```bash
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_= model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 10, use_cache = True)
+Hate speech, personal attacks, and discrimination
+```