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Model: teguhnandi/nandi
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+base_model: fine-tuned-llm-1B
+tags:
+- llm
+- lora
+- qlora
+- fine-tuning
+- health
+- chatbot
+- indonesian
+language:
+- id
+license: mit
+---
+
+# Chatbot Kesehatan Bahasa Indonesia (Fine-tuned LLM 1B)
+
+Ini adalah model bahasa besar (LLM) 1 miliar parameter yang telah di-fine-tune menggunakan teknik **QLoRA 4-bit Quantization** dengan dataset berbahasa Indonesia yang berfokus pada **informasi kesehatan**. Model ini dirancang untuk berfungsi sebagai chatbot asisten AI di bidang kesehatan secara umum.
+
+## Tujuan
+Model ini dioptimalkan untuk memberikan jawaban yang akurat dan helpful terkait pertanyaan-pertanyaan seputar kesehatan dalam Bahasa Indonesia. Fine-tuning ini bertujuan untuk membuat model lebih ahli dalam domain medis spesifik ini.
+
+## Dataset
+Dataset yang digunakan adalah kumpulan data berbahasa Indonesia terkait berbagai topik kesehatan, yang berisi berbagai intent, pola pertanyaan, dan respons relevan.
+
+## Parameter Fine-tuning
+- **Base Model**: Llama 3.2 1B Instruct (digunakan sebagai model dasar untuk fine-tuning)
+- **Teknik**: QLoRA (4-bit quantization)
+- **LoRA Config**:
+  - `r=16`
+  - `lora_alpha=32`
+  - `lora_dropout=0.05`
+  - `target_modules`: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+- **Training Arguments**:
+  - `num_train_epochs=3`
+  - `learning_rate=2e-4`
+  - `per_device_train_batch_size=2`
+  - `gradient_accumulation_steps=8`
+  - `fp16=True`
+  - `gradient_checkpointing=True`
+
+## Penggunaan (Inference)
+
+Anda dapat memuat model ini menggunakan library `transformers`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "teguhnandi/rijanandi"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16, # Pastikan menggunakan torch.float16 atau bfloat16
+    device_map='auto',
+)
+
+def generate_response(prompt, model, tokenizer, max_length=256):
+    system_prompt = """Anda adalah asisten AI yang ahli dalam kesehatan dan perawatan. Berikan jawaban yang akurat, helpful, dan berdasarkan pengetahuan medis yang terpercaya. Jika tidak yakin, sarankan untuk berkonsultasi dengan profesional kesehatan."""
+
+    full_prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+"""
+
+    inputs = tokenizer(full_prompt, return_tensors='pt').to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_length,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    if "<|start_header_id|>assistant<|end_header_id|>" in response:
+        response = response.split("<|start_header_id|>assistant<|end_header_id|>
+")[-1]
+    response = response.replace("<|eot_id|>", "").strip()
+    return response
+
+# Contoh penggunaan
+question = "Apa saja tips menjaga kesehatan secara umum?"
+answer = generate_response(question, model, tokenizer)
+print(f"Pertanyaan: {question}")
+print(f"Jawaban: {answer}")
+```
+
+## Catatan
+Model ini masih dalam tahap pengembangan dan dapat ditingkatkan lebih lanjut dengan dataset yang lebih luas dan fine-tuning lanjutan.

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,
+  "dtype": "float16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "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": "5.0.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1bcebe4e712578856786ad252b538e5cceb68759de3ecca844f517b8e612a8e8
+size 2471645464

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}