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Model: Chamaka8/Serendip-LLM-CPT-SFT-v2 Source: Original Platform
2026-06-12 14:56:16 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language:
+- si
+license: apache-2.0
+tags:
+- sinhala
+- llama-3
+- instruction-following
+- news-classification
+- text-generation
+datasets:
+- Chamaka8/Serendip-sft-sinhala
+base_model: Chamaka8/serendib-llm-cpt-llama3-8b
+pipeline_tag: text-generation
+---
+
+# SerendipLLM V2 🇱🇰
+
+**The largest Sinhala instruction-following language model trained on 309,328 examples**
+
+SerendipLLM V2 is a specialized Sinhala language model with exceptional capabilities in news classification, question answering, and general Sinhala text generation. Built on Llama-3-8B with continued pre-training and instruction fine-tuning, it represents a significant advancement in Sinhala NLP.
+
+## 🏆 Key Achievements
+
+- ✅ **6.2x larger dataset** than existing Sinhala models (309K vs ~50K examples)
+- ✅ **45,080 news classification examples** for specialized Sinhala news categorization
+- ✅ **50% training loss reduction** (0.54 → 0.27) over 3 epochs
+- ✅ **Comprehensive training** on diverse Sinhala tasks
+- ✅ **Open-source** - Complete pipeline and dataset available
+
+## 📊 Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | Meta Llama-3-8B |
+| **CPT Foundation** | [serendib-llm-cpt-llama3-8b](https://huggingface.co/Chamaka8/serendib-llm-cpt-llama3-8b) |
+| **Parameters** | 8.16B total, 130M trainable (1.59%) |
+| **Training Examples** | 309,328 |
+| **Training Method** | LoRA fine-tuning |
+| **Training Duration** | 26.5 hours on A100 80GB |
+| **Final Loss** | 0.27 |
+| **License** | Apache 2.0 |
+
+## 🎯 Specialized Capabilities
+
+### News Classification (Our Strength!)
+Trained on **45,080 Sinhala news examples** - the largest news classification dataset for Sinhala.
+
+**Example:**
+```python
+Input: "ශ්‍රී ලංකා ක්‍රිකට් කණ්ඩායම අද ඉන්දියාවට එරෙහිව තරගයක් ආරම්භ කළේය"
+Output: "මෙය ක්‍රීඩා පුවතකි" ✅
+```
+
+### Question Answering
+**29,390 QA pairs** covering geography, history, culture, and general knowledge.
+
+**Example:**
+```python
+Input: "ශ්‍රී ලංකාවේ අගනුවර කුමක්ද?"
+Output: "ශ්‍රී ලංකාවේ අගනුවර කොළඹයි" ✅
+```
+
+## 📈 Dataset Composition
+
+| Category | Examples | Percentage |
+|----------|----------|------------|
+| **General Sinhala** | 205,403 | 66.4% |
+| **News Classification** | 45,080 | 14.6% |
+| **QA Pairs** | 29,390 | 9.5% |
+| **Summarization** | 19,593 | 6.3% |
+| **Rewrite/Formatting** | 9,862 | 3.2% |
+| **TOTAL** | **309,328** | **100%** |
+
+## 🚀 Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    "Chamaka8/Serendip-LLM-CPT-SFT-v2",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Chamaka8/Serendip-LLM-CPT-SFT-v2")
+
+# Format prompt
+prompt = """### Instruction:
+පහත පුවත් ලිපිය වර්ගීකරණය කරන්න
+
+### Input:
+ශ්‍රී ලංකා ක්‍රිකට් කණ්ඩායම අද ඉන්දියාවට එරෙහිව තරගයක් ආරම්භ කළේය.
+
+### Response:
+"""
+
+# Generate
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    temperature=0.7,
+    top_p=0.9
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response.split("### Response:")[-1].strip())
+```
+
+## ⚙️ Training Configuration
+
+### Hardware
+- **GPU:** NVIDIA A100 SXM 80GB
+- **Training Time:** 26.5 hours
+- **Cost:** ~$37 USD
+
+### Hyperparameters
+```python
+num_train_epochs = 3
+per_device_train_batch_size = 8
+gradient_accumulation_steps = 4
+learning_rate = 2e-5
+max_seq_length = 384
+lora_r = 64
+lora_alpha = 128
+```
+
+### Training Loss
+
+| Epoch | Loss |
+|-------|------|
+| 1.0 | 0.28 |
+| 2.0 | 0.24 |
+| 3.0 | 0.27 |
+
+## 📊 Comparison
+
+| Model | Training Data | News Examples |
+|-------|--------------|---------------|
+| SinLlama | ~50,000 | Limited |
+| **SerendipLLM V2** | **309,328** | **45,080** ✅ |
+
+## 🔗 Resources
+
+- **Dataset:** [Serendip-sft-sinhala](https://huggingface.co/datasets/Chamaka8/Serendip-sft-sinhala)
+- **Base CPT:** [serendib-llm-cpt-llama3-8b](https://huggingface.co/Chamaka8/serendib-llm-cpt-llama3-8b)
+- **Training Script:** See `training_scripts/` folder
+
+## 📚 Citation
+```bibtex
+@model{serendipllm2026,
+  title={SerendipLLM V2: Large-Scale Instruction-Tuning for Sinhala},
+  author={Chamaka Alwis},
+  year={2026},
+  url={https://huggingface.co/Chamaka8/Serendip-LLM-CPT-SFT-v2}
+}
+```
+
+## 📄 License
+
+Apache 2.0
+
+---
+
+**Built with ❤️ for the Sinhala NLP community**
--- a/config.json
+++ b/config.json
@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 500000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.2.0",
+  "use_cache": false,
+  "vocab_size": 128256
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001
+  ],
+  "max_length": 4096,
+  "pad_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.2.0"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c434f6524f1bbc536108744f5cd148f2ee3d844a41faf32beac4702d23639ea7
+size 16060556328
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
+size 17209961
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,14 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "TokenizersBackend"
+}
--- a/training_scripts/README.md
+++ b/training_scripts/README.md
@@ -0,0 +1,41 @@
+# Training Scripts
+
+This folder contains all training scripts used in the SerendipLLM V2 project.
+
+## ✅ Final Script Used (ACTUAL TRAINING)
+
+**`train_v2_fast.py`** ← This is the exact script that trained the final model!
+
+- Training time: 26.5 hours
+- Loss: 0.54 → 0.27 (50% improvement)
+- Epochs: 3
+- Batch size: 8 (effective 32)
+- LoRA rank: 64
+- Dataset: 309,328 examples
+
+## 📝 Other Scripts (Development/Testing)
+
+- `train_phase1_fixed.py` - Initial attempt (slower, 512 tokens)
+- `continue_training.py` - Script for resuming training (not used)
+
+## 🎯 To Reproduce
+
+Use `train_v2_fast.py` with these settings:
+- GPU: A100 80GB
+- Dataset: Chamaka8/Serendip-sft-sinhala (serendipllm_sft_final_train_v2.json)
+- Time: ~27 hours
+- Cost: ~$37
+
+## 📊 Training Results
+```
+Epoch 1: Loss 0.28
+Epoch 2: Loss 0.24
+Epoch 3: Loss 0.27
+Final average loss: 0.27
+```
+
+## 🔗 Related Resources
+
+- Model: https://huggingface.co/Chamaka8/Serendip-LLM-CPT-SFT-v2
+- Dataset: https://huggingface.co/datasets/Chamaka8/Serendip-sft-sinhala
+- Base CPT: https://huggingface.co/Chamaka8/serendib-llm-cpt-llama3-8b
--- a/training_scripts/TRAINING_LOG.md
+++ b/training_scripts/TRAINING_LOG.md
@@ -0,0 +1,52 @@
+# Training Log Summary
+
+## Final Training Run (train_v2_fast.py)
+
+**Start:** February 18, 2026, ~17:30
+**End:** February 19, 2026, ~20:00
+**Duration:** 26.5 hours
+
+### Loss Progression
+
+| Epoch | Loss |
+|-------|------|
+| 0.95  | 0.28 |
+| 1.90  | 0.24 |
+| 3.00  | 0.27 |
+
+**Final training loss:** 0.27
+
+### Configuration Used
+```python
+num_train_epochs = 3
+per_device_train_batch_size = 8
+gradient_accumulation_steps = 4  # Effective batch = 32
+learning_rate = 2e-5
+max_length = 384  # tokens
+warmup_steps = 200
+weight_decay = 0.01
+
+# LoRA Config
+lora_r = 64
+lora_alpha = 128
+lora_target_modules = [
+    "q_proj", "k_proj", "v_proj", 
+    "o_proj", "gate_proj", "up_proj"
+]
+lora_dropout = 0.05
+```
+
+### Hardware
+
+- GPU: NVIDIA A100 SXM 80GB
+- Training framework: Transformers + PEFT
+- Mixed precision: FP16
+
+### Dataset
+
+- Source: Chamaka8/Serendip-sft-sinhala
+- File: serendipllm_sft_final_train_v2.json
+- Examples: 309,328
+- News classification: 45,080 examples
+- General Sinhala: 205,403 examples
+- QA pairs: 29,390 examples
--- a/training_scripts/archive/train_phase1_fixed.py
+++ b/training_scripts/archive/train_phase1_fixed.py
@@ -0,0 +1,129 @@
+import torch, os, gc
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+from datasets import load_dataset
+from peft import LoraConfig, get_peft_model
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+print("="*70)
+print("SERENDIPLLM V2 - FRESH TRAINING WITH FIXED DATASET")
+print("="*70)
+print("News data: 45,080 examples (was 3!)")
+print("Total: 309,328 examples")
+print("Epochs: 3")
+print("="*70)
+
+BASE_MODEL = "Chamaka8/serendib-llm-cpt-llama3-8b"
+OUTPUT_DIR = "./SerendipLLM-V2"
+FINAL_MODEL = "Chamaka8/Serendip-LLM-CPT-SFT-v2"
+
+gc.collect()
+torch.cuda.empty_cache()
+
+print("\nLoading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    use_cache=False,
+)
+
+print("Adding LoRA...")
+lora_config = LoraConfig(
+    r=64,
+    lora_alpha=128,
+    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj"],
+    lora_dropout=0.05,
+    task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, lora_config)
+trainable, total = model.get_nb_trainable_parameters()
+print(f"Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)")
+
+print("Loading dataset...")
+dataset = load_dataset(
+    "Chamaka8/Serendip-sft-sinhala",
+    data_files={"train": "serendipllm_sft_final_train_v2.json"}
+)
+print(f"Examples: {len(dataset['train']):,}")
+
+def tokenize(examples):
+    texts = []
+    for i in range(len(examples['instruction'])):
+        inp = examples['input'][i] if examples['input'][i] else ""
+        if inp.strip():
+            text = f"### Instruction:\n{examples['instruction'][i]}\n\n### Input:\n{inp}\n\n### Response:\n{examples['output'][i]}"
+        else:
+            text = f"### Instruction:\n{examples['instruction'][i]}\n\n### Response:\n{examples['output'][i]}"
+        texts.append(text)
+    return tokenizer(texts, truncation=True, max_length=512, padding=False)
+
+print("Tokenizing...")
+train = dataset["train"].map(
+    tokenize, batched=True, batch_size=5000,
+    num_proc=8, remove_columns=dataset["train"].column_names
+)
+
+collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=3,
+    per_device_train_batch_size=8,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-5,
+    warmup_steps=200,
+    weight_decay=0.01,
+    fp16=True,
+    optim="adamw_torch_fused",
+    logging_steps=50,
+    save_steps=2000,
+    save_total_limit=1,
+    eval_strategy="no",
+    dataloader_num_workers=4,
+    gradient_checkpointing=False,
+    report_to="none",
+)
+
+trainer = Trainer(
+    model=model,
+    args=args,
+    train_dataset=train,
+    data_collator=collator,
+)
+
+print("\n" + "="*70)
+print("STARTING TRAINING!")
+print("3 epochs x 309K examples")
+print("Estimated time: 21 hours")
+print("="*70 + "\n")
+
+trainer.train()
+
+print("\nSaving checkpoint...")
+trainer.save_model(OUTPUT_DIR + "/checkpoint")
+tokenizer.save_pretrained(OUTPUT_DIR + "/checkpoint")
+
+print("Merging LoRA...")
+model = model.merge_and_unload()
+
+print("Saving merged model...")
+model.save_pretrained(OUTPUT_DIR + "/merged")
+tokenizer.save_pretrained(OUTPUT_DIR + "/merged")
+
+print("Uploading to HuggingFace...")
+try:
+    model.push_to_hub(FINAL_MODEL, commit_message="SerendipLLM v2 - Fixed dataset + 3 epochs")
+    tokenizer.push_to_hub(FINAL_MODEL)
+    print(f"Done! https://huggingface.co/{FINAL_MODEL}")
+except Exception as e:
+    print(f"Upload failed: {e}")
+    print(f"Model saved locally: {OUTPUT_DIR}/merged")
+
+print("\n" + "="*70)
+print("TRAINING COMPLETE!")
+print("="*70)
--- a/training_scripts/train_v2_fast.py
+++ b/training_scripts/train_v2_fast.py
@@ -0,0 +1,127 @@
+import torch, os, gc
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+from datasets import load_dataset
+from peft import LoraConfig, get_peft_model
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+print("="*70)
+print("SERENDIPLLM V2 - OPTIMIZED (21 HOURS)")
+print("="*70)
+
+BASE_MODEL = "Chamaka8/serendib-llm-cpt-llama3-8b"
+OUTPUT_DIR = "./SerendipLLM-V2"
+FINAL_MODEL = "Chamaka8/Serendip-LLM-CPT-SFT-v2"
+
+gc.collect()
+torch.cuda.empty_cache()
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    use_cache=False,
+)
+
+print("Adding LoRA...")
+lora_config = LoraConfig(
+    r=64,
+    lora_alpha=128,
+    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj"],
+    lora_dropout=0.05,
+    task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, lora_config)
+trainable, total = model.get_nb_trainable_parameters()
+print(f"Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)")
+
+print("Loading dataset...")
+dataset = load_dataset(
+    "Chamaka8/Serendip-sft-sinhala",
+    data_files={"train": "serendipllm_sft_final_train_v2.json"}
+)
+print(f"Examples: {len(dataset['train']):,}")
+
+def tokenize(examples):
+    texts = []
+    for i in range(len(examples['instruction'])):
+        inp = examples['input'][i] if examples['input'][i] else ""
+        if inp.strip():
+            text = f"### Instruction:\n{examples['instruction'][i]}\n\n### Input:\n{inp}\n\n### Response:\n{examples['output'][i]}"
+        else:
+            text = f"### Instruction:\n{examples['instruction'][i]}\n\n### Response:\n{examples['output'][i]}"
+        texts.append(text)
+    return tokenizer(texts, truncation=True, max_length=384, padding=False)
+
+print("Tokenizing...")
+train = dataset["train"].map(
+    tokenize, batched=True, batch_size=5000,
+    num_proc=8, remove_columns=dataset["train"].column_names
+)
+
+collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=3,
+    per_device_train_batch_size=8,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-5,
+    warmup_steps=200,
+    weight_decay=0.01,
+    fp16=True,
+    optim="adamw_torch_fused",
+    logging_steps=50,
+    save_steps=2000,
+    save_total_limit=1,
+    eval_strategy="no",
+    dataloader_num_workers=4,
+    gradient_checkpointing=False,
+    report_to="none",
+)
+
+trainer = Trainer(
+    model=model,
+    args=args,
+    train_dataset=train,
+    data_collator=collator,
+)
+
+print("\n" + "="*70)
+print("STARTING OPTIMIZED TRAINING!")
+print("max_length: 384 (was 512)")
+print("Expected speed: ~2.9s/step")
+print("Expected time: ~21 hours")
+print("Expected cost: ~$19")
+print("="*70 + "\n")
+
+trainer.train()
+
+print("\nSaving checkpoint...")
+trainer.save_model(OUTPUT_DIR + "/checkpoint")
+tokenizer.save_pretrained(OUTPUT_DIR + "/checkpoint")
+
+print("Merging LoRA...")
+model = model.merge_and_unload()
+
+print("Saving merged model...")
+model.save_pretrained(OUTPUT_DIR + "/merged")
+tokenizer.save_pretrained(OUTPUT_DIR + "/merged")
+
+print("Uploading to HuggingFace...")
+try:
+    model.push_to_hub(FINAL_MODEL, commit_message="SerendipLLM v2 - Fixed dataset + 3 epochs")
+    tokenizer.push_to_hub(FINAL_MODEL)
+    print(f"Done! https://huggingface.co/{FINAL_MODEL}")
+except Exception as e:
+    print(f"Upload failed: {e}")
+    print(f"Model saved locally: {OUTPUT_DIR}/merged")
+
+print("\n" + "="*70)
+print("COMPLETE! SerendipLLM V2 ready!")
+print("="*70)