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Model: Lavanya177/SkyAssist-Llama
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+language:
+- en
+
+library_name: transformers
+
+pipeline_tag: text-generation
+
+base_model: meta-llama/Llama-3.2-3B-Instruct
+
+tags:
+- llama
+- qlora
+- peft
+- airline
+- customer-support
+- conversational
+- instruction-tuning
+---
+
+# ✈️ SkyAssist-Llama
+
+SkyAssist-Llama is a domain-specific airline customer support large language model created by fine-tuning **Meta Llama 3.2 3B Instruct** using **QLoRA (Quantized Low-Rank Adaptation)**.
+
+The model is designed to generate professional, empathetic, and context-aware responses for common airline customer support scenarios, including flight delays, cancellations, baggage inquiries, booking modifications, refunds, and check-in assistance.
+
+---
+
+# Model Details
+
+| Property | Value |
+|----------|-------|
+| Base Model | Meta Llama 3.2 3B Instruct |
+| Fine-tuning Method | QLoRA |
+| Task | Airline Customer Support |
+| Language | English |
+| Architecture | Decoder-only Transformer |
+| Training Objective | Supervised Fine-Tuning (SFT) |
+
+---
+
+# Training Data
+
+The model was trained on **2,666** airline customer support conversations.
+
+Rather than relying on an existing airline dataset, the training data was created through a multi-stage automated pipeline that transformed general customer support conversations into realistic airline-specific interactions.
+
+The dataset generation process included:
+
+- Data preprocessing
+- LLM-based convertibility classification
+- Airline domain transformation
+- post-processing and cleaning
+- JSON validation
+- Sample-based manual review
+
+The complete dataset is available on Hugging Face:
+
+**Dataset:** https://huggingface.co/datasets/Lavanya177/airline-support-tone-clean
+
+---
+
+# Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Llama 3.2 3B Instruct |
+| Fine-Tuning Method | QLoRA |
+| LoRA Rank | 32 |
+| LoRA Alpha | 64 |
+| Learning Rate | 1e-3 |
+| Epochs | 3 |
+| Batch Size | 8 |
+| Gradient Accumulation | 4 |
+| Optimizer | AdamW |
+| Scheduler | Cosine |
+| Max Sequence Length | 2048 |
+| Precision | BF16 |
+
+---
+
+# Evaluation
+
+| Metric | Value |
+|--------|------:|
+| Training Loss | **0.4615** |
+| Validation Loss | **0.5557** |
+| Test Loss | **0.62** |
+
+The model demonstrates good convergence while maintaining strong generalization on unseen airline customer support conversations.
+
+---
+
+# Intended Use
+
+SkyAssist-Llama is intended for:
+
+- Airline customer support assistants
+- Conversational AI research
+- Educational purposes
+- Domain adaptation experiments
+- LLM fine-tuning research
+
+---
+
+# Limitations
+
+This model:
+
+- does not access live airline booking systems
+- cannot retrieve reservation details
+- cannot process ticket changes or refunds
+- may generate incorrect airline-specific policies
+- should not replace official airline customer support
+
+---
+
+# Usage
+
+## Load the model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "Lavanya177/SkyAssist-Llama"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto"
+)
+```
+---
+
+# Example
+
+**System Prompt**
+
+```
+You are a professional airline customer support assistant...
+```
+
+**User**
+
+```
+My baggage has not arrived. What should I do?
+```
+
+**Assistant**
+
+```
+I'm sorry to hear that your baggage has not arrived. I understand how frustrating this situation can be.
+
+Please provide your baggage claim number, flight number, travel date, and destination airport so I can assist you further. If you have already reported the issue at the airport, please also share your Property Irregularity Report (PIR) reference number.
+
+Once these details are available, the airline can begin tracking your baggage and provide updates on its status.
+```
+
+---
+
+# Citation
+
+```bibtex
+@misc{skyassistllama2026,
+  title={SkyAssist-Llama: A Domain-Specific Airline Customer Support Large Language Model},
+  author={Lavanya Singh},
+  year={2026}
+}
+```
+
+# License
+
+This project is released under the MIT License.

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": "bfloat16",
+  "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,
+  "pad_token_id": 128009,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "bfloat16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "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,
+  "transformers_version": "4.57.1",
+  "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
+  ],
+  "pad_token_id": 128009,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57d20ed0ec8804435c3b1d0dc5d55f2591205db725b60a616122de5b93dc02b2
+size 2242762591

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}