abhinand
/

telugu-llama-7b-instruct-v0.1

@@ -1,165 +1,151 @@
 ---
-library_name: peft
-tags:
-- axolotl
-- generated_from_trainer
 base_model: abhinand/dr-llama-te-instruct-v0
 model-index:
-- name: dr-llama-te-instruct-v0-lora-ext
   results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-<details><summary>See axolotl config</summary>
-axolotl version: `0.3.0`
-```yaml
-base_model: abhinand/dr-llama-te-instruct-v0
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-trust_remote_code: true
-is_llama_derived_model: true
-# huggingface repo
-datasets:
-  - path: abhinand/telugu_llama_instruct
-    name: regional_sharegpt_gs8
-    type: sharegpt.load_role
-    conversation: chatml
-    train_on_split: train
-  - path: abhinand/detox-dpo-te
-    name: sharegpt_gs8
-    type: sharegpt.load_role
-    conversation: chatml
-    train_on_split: train
-load_in_4bit: false
-load_in_8bit: false
-bf16: true # require >=ampere
-chat_template: chatml
-dataset_prepared_path: last_run_prepared_path
-hub_model_id: abhinand/dr-llama-te-instruct-v0-lora-ext
-group_by_length: false
-val_set_size: 0.0
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-adapter: lora
-lora_model_dir:
-lora_r: 64
-lora_alpha: 128
-lora_target_modules:
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-  - gate_proj
-  - down_proj
-  - up_proj
-lora_modules_to_save:
-  - embed_tokens
-  - lm_head
-lora_dropout: 0.1
-lora_target_linear: true
-lora_fan_in_fan_out:
-output_dir: /home/dev/axolotl/saved_models/telugu-instruct-extended
-gradient_accumulation_steps: 8
-micro_batch_size: 4
-eval_batch_size: 4
-num_epochs: 1
-logging_steps: 1
-save_steps: 10
-save_total_limit: 3
-save_safetensors: false
-gradient_checkpointing: true
-lr_scheduler: cosine
-optimizer: "adamw_bnb_8bit"
-adam_beta2: 0.95
-adam_epsilon: 0.00001
-weight_decay: 0.1
-learning_rate: 0.0005
-max_grad_norm: 1.0
-warmup_ratio: 0.05
-# warmup_steps: 10
-flash_attention: true
-# Resume from a specific checkpoint dir
-resume_from_checkpoint:
-# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
-# Be careful with this being turned on between different models.
-# auto_resume_from_checkpoints: true
-# wandb configuration if you're using it
-# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
-wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
-wandb_project: "telugu-llama-sft"
-wandb_name:
-wandb_run_id:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-tokens: # these are delimiters
-  - "<|im_start|>"
-  - "<|im_end|>"
 ```
-</details><br>
-# dr-llama-te-instruct-v0-lora-ext
-This model is a fine-tuned version of [abhinand/dr-llama-te-instruct-v0](https://huggingface.co/abhinand/dr-llama-te-instruct-v0) on the None dataset.
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 0.0005
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 32
-- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 3
-- num_epochs: 1
-### Training results
-### Framework versions
-- PEFT 0.7.0
-- Transformers 4.37.0.dev0
-- Pytorch 2.0.1+cu117
-- Datasets 2.16.1
-- Tokenizers 0.15.0

 ---
 base_model: abhinand/dr-llama-te-instruct-v0
 model-index:
+- name: telugu-llama-instruct-v0.1
   results: []
+license: gpl-3.0
+language:
+- en
+- te
 ---
+# Telugu LLaMA 7B Instruct v0.1
+Welcome to the inaugural release of the Telugu LLaMA 7B instruct model – an important step in advancing LLMs for the Telugu language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks.
+To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP)]() that outlines our journey and the model's potential impact.
+> **Note:** This model is based on the Tamil LLaMA series of models. The GitHub repository remains the same - [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama). The base models and the updated code for Tamil LLaMA v0.2 (which this work is based on) will be released soon.
+## Model description
+The Telugu LLaMA models have been enhanced and tailored specifically with an extensive Telugu vocabulary of ~16,000 tokens, building upon the foundation set by the original LLaMA-2.
+- **Model type:** A 7B parameter GPT-like model finetuned on ~500,000 samples consisting of an equal proportion of English and Telugu samples.
+- **Language(s):** Bilingual. English and Telugu.
+- **License:** GNU General Public License v3.0
+- **Finetuned from model:** [To be released soon]()
+- **Training Precision:** `bfloat16`
+- **Code:** [GitHub](https://github.com/abhinand5/tamil-llama) (To be updated soon)
+## Prompt Template: ChatML
+```
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
 ```
+## Benchmark Results
+Benchmarking was done using [LLM-Autoeval](https://github.com/mlabonne/llm-autoeval) on an RTX 3090 on [runpod](https://www.runpod.io/).
+![Benchmark results](https://cdn-uploads.huggingface.co/production/uploads/60c8619d95d852a24572b025/P_0y9pvGg2Gn1Uz7SSJ7E.png)
+## Related Models
+| Model                    | Type                        | Data              | Base Model           | # Params | Download Links                                                         |
+|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
+| Tamil LLaMA 7B v0.1 Base      | Base model                  | 12GB              | LLaMA 7B             | 7B   | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-base-v0.1)     |
+| Tamil LLaMA 13B v0.1 Base     | Base model                  | 4GB               | LLaMA 13B            | 13B  | [HF Hub](https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1)    |
+| Tamil LLaMA 7B v0.1 Instruct  | Instruction following model | 145k instructions | Tamil LLaMA 7B Base  | 7B   | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) |
+| Tamil LLaMA 13B v0.1 Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B  | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1)                       |
+| Tamil LLaMA 7B v0.2 Instruct | Instruction/Chat model | 500k instructions | Tamil LLaMA 7B Base v0.2 | 7B  | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) |
+| Malayalam LLaMA 7B v0.2 Instruct | Instruction/Chat model | 500k instructions | Malayalam LLaMA 7B Base v0.1 | 7B  | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) |
+## Example Usage
+```python
+from transformers import LlamaForCausalLM, AutoTokenizer, pipeline
+model = LlamaForCausalLM.from_pretrained(
+    "abhinand/telugu-llama-instruct-v0.1",
+    #load_in_8bit=True, # Set this depending on the GPU you have
+    torch_dtype=torch.bfloat16,
+    device_map={"": 0}, # Set this depending on the number of GPUs you have
+    local_files_only=False # Optional
+)
+model.eval()
+tokenizer = AutoTokenizer.from_pretrained("abhinand/telugu-llama-instruct-v0.1")
+inf_pipeline = pipeline("conversational", model=model, tokenizer=tokenizer)
+def format_instruction(system_prompt, question, return_dict=False):
+	if system_prompt is None:
+		messages = [
+			{'content': question, 'role': 'user'},
+		]
+	else:
+		messages = [
+			{'content': system_prompt, 'role': 'system'},
+			{'content': question, 'role': 'user'},
+		]
+	if return_dict:
+		return messages
+	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+	return prompt
+# Set the generation configuration according to your needs
+temperature = 0.6
+repetition_penalty = 1.1
+max_new_tokens = 256
+SYSTEM_PROMPT = "You are an AI assistant who follows instructions extremely well. Do your best your best to help."
+INPUT = "Who were the Nizams of Hyderabad?"
+instruction = format_instruction(
+    system_prompt=SYSTEM_PROMPT,
+    question=INPUT,
+    return_dict=True,
+)
+output = inf_pipeline(
+    instruction,
+    temperature=temperature,
+    max_new_tokens=max_new_tokens,
+    repetition_penalty=repetition_penalty
+)
+print(output)
+# =========== EXAMPLE OUTPUT ===========
+# Conversation id: d57cdf33-01ff-4328-8efe-5c4fefdd6e77
+# system: You are an AI assistant who follows instructions extremely well. Do your best your best to help.
+# user: Who were the Nizams of Hyderabad?
+# assistant: The Nizams of Hyderabad were a dynasty that ruled the Deccan Plateau in southern India, including the city of Hyderabad. They were known for their wealth and patronage of art and culture. The last Nizam, Mir Osman Ali Khan, was one of the richest people in the world at the time of his death in 1967.
+# ======================================
+```
+## Usage Note
+It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
+## Meet the Developers
+Get to know the creators behind this innovative model and follow their contributions to the field:
+- [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/)
+## Citation
+If you use this model or any of the the Tamil-Llama related work in your research, please cite:
+```bibtex
+@misc{balachandran2023tamilllama,
+      title={Tamil-Llama: A New Tamil Language Model Based on Llama 2},
+      author={Abhinand Balachandran},
+      year={2023},
+      eprint={2311.05845},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.