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Model: iCIIT/redqueenprotocol-sin-llama3.2-3B-model
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+language:
+- si
+library_name: transformers
+tags:
+- llama-3
+- sinhala
+- generative-qa
+- iciit-2025
+- lora
+datasets:
+- RedQueenProtocol/all-articles-from-sinhala-wikipedia-2025-parquet
+- RedQueenProtocol/sinhala-qna-530-rows
+- ihalage/sinhala-finetune-qa-eli5
+- janani-rane/SiQuAD
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+---
+
+
+# RedQueen Llama 3.2 3B - Sinhala Generative QA
+
+**Technical Report:** [Click here for pdf](https://drive.google.com/file/d/1XFPwiwTx5j8yxcBCxmyDZgK5ldpulFw-/view?usp=sharing)
+<br>
+**GitHub Repo for Scripts and Notebooks:** [Click here](https://github.com/scythe410/Below-8B-Sinhala-LLM-Training---RedQueen-Protocol)
+
+- **Developed by:** [Red Queen Protocol](https://huggingface.co/RedQueenProtocol)
+- **Team:** [Ramiru De Silva](https://www.linkedin.com/in/ramirudesilva/), [Senadhi Thimanya](https://www.linkedin.com/in/senadhi-chandrasekara/)
+- **Language(s) (NLP):** Sinhala
+- **Finetuned from model:** [Llama 3.2 3B IT](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
+
+
+
+This model and LoRA was developed by Ramiru De Silva and Senadhi Thimanya (Team: [RedQueen Protocol](https://huggingface.co/RedQueenProtocol)) for the iCIIT Conclave 2025 Shared Task on Building Compact Sinhala & Tamil LLMs. 
+This is a 3-billion parameter, instruction-tuned model that has undergone a novel two-stage fine-tuning process to achieve proficiency in both the Sinhala language and the specific task of generative QA. The entire fine-tuning process was performed efficiently using Low-Rank Adaptation (LoRA) technique.
+<br>
+The model's creation follows a hierarchical training strategy designed to first build a strong linguistic foundation and then specialize it for a specific task.
+
+### Stage 1: Domain Adaptation (Language Foundation)
+The initial model, `RedQueenProtocol/llama-3.2-3b-it-sinhala-rq` (Meta's Llama-3.2-3B-IT copies into a private repo for ease of use), was fine-tuned on the entirety of the Sinhala Wikipedia to create a foundational model with a comprehensive grasp of the language.
+- **Dataset:** `RedQueenProtocol/all-articles-from-sinhala-wikipedia-2025-parquet`.
+- **Method:** Long articles were tokenized and split into overlapping chunks of 512 tokens to ensure full context was seen during training.
+- **Output Model:** The resulting adapter was merged to create the Sinhala domain-expert base model for the next stage: `RedQueenProtocol/sinhala-wiki-2025-LoRA-merged`.
+
+### Stage 2: Task Adaptation (Sequential QA Fine-tuning)
+Using the Wikipedia-tuned model as the new base, a single LoRA adapter was sequentially fine-tuned on three distinct QA datasets to progressively accumulate question-answering skills.
+<br>
+The training sequence was as follows:
+1. **Custom Dataset:** Fine-tuned on a manually curated dataset of 528 Sinhala QA pairs (`RedQueenProtocol/sinhala-qna-530-rows`).
+2. **Ihalage ELI5 Dataset:** Continued training the same adapter on 10,000 samples from the `ihalage/sinhala-finetune-qa-eli5` dataset.
+3. **SiQuAD Dataset:** Performed a final round of training on 13,500 samples from the `janani-rane/SiQuAD` dataset, formatting the inputs as "Context: ... Question: ... Answer: ...".
+
+The **final LoRA adapter**, containing the combined knowledge of all three datasets **and the Wikipedia-tuned base model** was then uploaded here in seperate repositories.
+
+## How to Use
+
+```python
+
+# For Kaggle:
+#from kaggle_secrets import UserSecretsClient
+#from huggingface_hub import login
+#user_secrets = UserSecretsClient()
+#hf_token = user_secrets.get_secret("HF_TOKEN")
+#login(token=hf_token)
+
+# For Colab:
+#from huggingface_hub import notebook_login
+#notebook_login()
+
+# --- 1. Install Libraries ---
+!pip install -q -U transformers accelerate bitsandbytes peft
+
+# --- 2. Import Libraries ---
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModel
+import warnings
+
+# --- 3. Configuration ---
+# Now both the base model and adapter are loaded from the iCIIT organization.
+base_model_id = "iCIIT/redqueenprotocol-sin-llama3.2-3B-model"
+adapter_id = "iCIIT/redqueenprotocol-sin-llama3.2-3B-LoRA"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# --- 4. Load Model and Adapter ---
+print(f"Loading base model from: {base_model_id}")
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map=device,
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+tokenizer.pad_token = tokenizer.eos_token
+
+print(f"Applying LoRA adapter from: {adapter_id}")
+model = PeftModel.from_pretrained(base_model, adapter_id)
+print("\n Model and adapter loaded successfully from the iCIIT repositories.")
+
+# --- 5. Run a Sample Prompt ---
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+question = "ශ්‍රී ලංකා ජාතික ධජය නිර්මාණය කළේ කවුද?"
+
+prompt = f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n\\n{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n"
+
+print("\n" + "="*50)
+print(f"USER: {question}")
+print("\nASSISTANT: Generating...")
+
+outputs = generator(
+    prompt,
+    max_new_tokens=256,
+    eos_token_id=tokenizer.eos_token_id,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+
+full_response = outputs[0]['generated_text']
+answer = full_response.split("<|start_header_id|>assistant<|end_header_id|>\\n\\n")[1].replace("<|eot_id|>", "")
+
+print(answer.strip())
+print("="*50)

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.54.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.54.1"
+}

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