commit files to HF hub

README.md

@@ -1,3 +1,122 @@
----
-license: mit
----
+---
+library_name: peft
+base_model: meta-llama/Llama-2-13b-chat-hf
+license: mit
+datasets:
+- irlab-udc/metahate
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- hate speech
+---
+
+# LLaMA2 Fine-Tuned on not Engaging with Hate Speech
+
+## Model Description
+This model is a fine-tuned version of `meta-llama/Llama-2-13b-chat-hf` on a hate speech dataset using the PEFT approach, to prevent the model from exacerbating hate discourse.
+
+## Intended Uses & Limitations
+This model is intended for research purposes in conversational applications to stop hate speech generation.
+
+## Bias, Risks, and Limitations
+
+- **Biases**: The model may carry biases present in the training data.
+- **False Positives/Negatives**: It's not perfect and may continue some hate speech conversations.
+- **Domain Specificity**: Performance may vary across different domains.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, Conversation, pipeline
+
+# Load the model
+config = PeftConfig.from_pretrained("irlab-udc/LLaMA2-13b-Stop-Hate")
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
+model = PeftModel.from_pretrained(base_model, "irlab-udc/LLaMA2-13b-Stop-Hate")
+tokenizer = AutoTokenizer.from_pretrained("irlab-udc/LLaMA2-13b-Stop-Hate")
+
+# Test the model
+chatbot = pipeline(task="conversational", model=model, tokenizer=tokenizer)
+conversation = Conversation("Your input text here")
+conversation = chatbot(conversation)
+result = conversation.messages[-1]["content"]
+```
+
+
+## Training Details
+- **Base Model:** meta-llama/Llama-2-13b-chat-hf
+- **Fine-Tuning:** Using PEFT approach
+- **Hardware:** NVIDIA RTX A6000
+  
+#### Configurations and Hyperparameters
+
+The following LoraConfig config was used during training:
+
+- r: 32
+- lora_alpha: 64
+- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head"]
+- lora_dropout: 0.05
+- bias: "lora_only"
+- task_type: "CAUSAL_LM"
+
+The following TrainingArguments config was used during training:
+
+- per_device_train_batch_size: 16
+- gradient_accumulation_steps: 1
+- warmup_steps: 5
+- max_steps: 1000
+- learning_rate: 2.5e-5
+- fp16=True
+- optim= paged_adamw_8bit
+
+The following `bitsandbytes` quantization config was used during training:
+
+- quant_method: bitsandbytes
+- _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: bfloat16
+- bnb_4bit_quant_storage: uint8
+- load_in_4bit: True
+- load_in_8bit: False
+
+### Framework versions
+
+- PEFT 0.6.2
+- PyTorch 2.1.0
+- 🤗 Transformers 4.35.0
+- 🤗 Datasets 2.14.6
+
+
+## Environmental Impact
+
+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:** NVIDIA RTX A6000
+- **Hours used:** 9
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Efficiency (kg/kWh):** 0,432
+- **Carbon Emitted (kg eq. CO2):** 1,17
+
+
+## Citation
+
+If you use this model, please cite the following reference:
+
+```bibtex
+@article{
+  SOON!
+}
+```
+
+## Acknowledgements
+The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).

adapter_config.json

@@ -0,0 +1,29 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-2-13b-chat-hf",
+  "bias": "lora_only",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "down_proj",
+    "v_proj",
+    "gate_proj",
+    "up_proj",
+    "k_proj",
+    "o_proj",
+    "lm_head"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a169f22c60115153293d1d0b3a9dbce6fc895043be641f1c6e5834ca0351e41
+size 505649618

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

@@ -0,0 +1,42 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": "</s>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}