Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 language:
 - en
+- hi
 license: apache-2.0
 tags:
 - text-generation-inference
@@ -9,14 +10,81 @@ tags:
 - llama
 - trl
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+datasets:
+- yahma/alpaca-cleaned
+- ravithejads/samvaad-hi-filtered
+- HydraIndicLM/hindi_alpaca_dolly_67k
 ---
 
-# Uploaded  model
+# TinyLlama-1.1B-Hinglish-LORA-v1.0 model
 
-- **Developed by:** kirankunapuli
+- **Developed by:** [Kiran Kunapuli](https://www.linkedin.com/in/kirankunapuli/)
 - **License:** apache-2.0
-- **Finetuned from model :** TinyLlama/TinyLlama-1.1B-Chat-v1.0
+- **Finetuned from model:** TinyLlama/TinyLlama-1.1B-Chat-v1.0
+- **Model config:**
+  ```python
+    model = FastLanguageModel.get_peft_model(
+    model,
+    r = 64, 
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 128,
+    lora_dropout = 0, 
+    bias = "none",   
+    use_gradient_checkpointing = True, 
+    random_state = 42,
+    use_rslora = True,  
+    loftq_config = None, 
+    )
+  ```
+- **Training parameters:**
+  ```python
+    trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    dataset_text_field = "text",
+    max_seq_length = max_seq_length,
+    dataset_num_proc = 2,
+    packing = True, 
+    args = TrainingArguments(
+        per_device_train_batch_size = 12,
+        gradient_accumulation_steps = 16,
+        warmup_ratio = 0.1,
+        num_train_epochs = 1,
+        learning_rate = 2e-4,
+        fp16 = not torch.cuda.is_bf16_supported(),
+        bf16 = torch.cuda.is_bf16_supported(),
+        logging_steps = 1,
+        optim = "paged_adamw_32bit",
+        weight_decay = 0.001,
+        lr_scheduler_type = "cosine",
+        seed = 42,
+        output_dir = "outputs",
+        report_to = "wandb",
+      ),
+    )
+  ```
+- **Training details:**
+  ```
+  ==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
+     \\   /|    Num examples = 15,464 | Num Epochs = 1
+  O^O/ \_/ \    Batch size per device = 12 | Gradient Accumulation steps = 16
+  \        /    Total batch size = 192 | Total steps = 80
+   "-____-"     Number of trainable parameters = 50,462,720
+
+  GPU = NVIDIA GeForce RTX 3090. Max memory = 24.0 GB.
+  Total time taken for 1 epoch - 2h:35m:28s
+  9443.5288 seconds used for training.
+  157.39 minutes used for training.
+  Peak reserved memory = 17.641 GB.
+  Peak reserved memory for training = 15.344 GB.
+  Peak reserved memory % of max memory = 73.504 %.
+  Peak reserved memory for training % of max memory = 63.933 %.
+  ```
 
 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)
+**[NOTE]** TinyLlama's internal maximum sequence length is 2048. We use RoPE Scaling to extend it to 4096 with Unsloth!
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)