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Model: yowww1094/tourism-llm-fine-tuned-qwen2-1.5b-lora-merged
Source: Original Platform

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README.md

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+---
+library_name: transformers
+license: mit
+datasets:
+- yowww1094/tourism-llm-fine-tuning-dataset
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- tourism
+- travel
+- question-answering
+- lora
+- fine-tuned
+- rag
+---
+
+# Tourism Assistant — Qwen2.5-1.5B Fine-Tuned
+
+A fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) trained on a custom tourism Q&A dataset generated through a RAG-grounded pipeline. Built as a personal end-to-end learning project covering data collection, dataset engineering, supervised fine-tuning, and deployment.
+
+> ⚠️ This model is not production-ready. It is a learning project. Outputs can be incorrect, incomplete, or inconsistent — especially on topics not well-represented in the training data. Do not rely on this model for real travel decisions.
+
+---
+
+## Model Details
+
+### Model Description
+
+This model was fine-tuned using FP16 LoRA (Low-Rank Adaptation) on a small custom dataset of ~150–200 tourism-focused question-answer pairs. The training examples were generated through a RAG pipeline: Reddit travel posts were scraped, embedded, and stored in a Qdrant vector store, then a locally-hosted Qwen2.5-7B (via Ollama) was used to generate grounded Q&A pairs from retrieved context chunks. The resulting dataset was formatted in ChatML and used to fine-tune this model.
+
+The goal of fine-tuning was to adjust the model's **behavioral style** — making it more focused, concise, and consistently helpful for travel queries — rather than to inject new factual knowledge. Factual grounding at inference time is handled by a RAG pipeline backed by Qdrant Cloud.
+
+- **Developed by:** Younes
+- **Model type:** Causal Language Model — fine-tuned for instruction following
+- **Language:** English
+- **License:** MIT
+- **Base model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+- **Fine-tuning method:** FP16 LoRA (merged into base weights)
+- **Training dataset:** [yowww1094/tourism-llm-fine-tuning-dataset](https://huggingface.co/datasets/yowww1094/tourism-llm-fine-tuning-dataset)
+
+### Model Sources
+
+- **Repository:** [Github](https://github.com/yowww1094/llm-tourism-assistant)
+- **Demo:** [Demo](https://huggingface.co/spaces/yowww1094/AI-tourism-chatbot)
+
+---
+
+## Uses
+
+### Direct Use
+
+This model can be used as a conversational assistant for general tourism and travel questions — destination information, logistics, visa guidance, packing advice, and similar topics. It works best when paired with a retrieval pipeline that provides relevant context at inference time.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "yowww1094/tourism-assistant-qwen2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a helpful tourism assistant."},
+    {"role": "user", "content": "What are the must-visit places in Marrakech?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+```
+
+### Downstream Use
+
+This model is designed to be used as the generation component of a RAG pipeline. The recommended usage is:
+
+1. Embed the user query with `all-MiniLM-L6-v2`
+2. Retrieve top-k relevant chunks from a Qdrant vector store
+3. Inject retrieved context into the prompt before generation
+4. Pass the full prompt to this model
+
+Pairing the model with retrieval significantly improves factual accuracy on specific travel queries compared to using the model standalone.
+
+### Out-of-Scope Use
+
+- **Real-time travel information:** The model has no access to live data. Flight prices, visa requirements, and safety conditions change frequently — do not rely on this model for current information.
+- **Medical or legal travel advice:** The model is not equipped to give reliable guidance on health requirements, legal restrictions, or emergency situations.
+- **Non-English queries:** The model was trained exclusively on English data and is not reliable for other languages.
+- **High-stakes decisions:** This is a learning project. Outputs should not be used to make actual travel bookings, visa applications, or safety assessments.
+
+---
+
+## Bias, Risks, and Limitations
+
+**Dataset limitations — primary source of inaccuracy:**
+The training dataset contains only ~150–200 examples sourced from Reddit travel communities. This introduces several compounding problems:
+
+- Reddit's user base skews toward English-speaking, Western travellers — advice reflects this demographic and may not generalise to other travel styles or origins
+- The dataset covers only a narrow slice of the tourism domain; large topic areas have no representation
+- Source Reddit posts were not independently fact-checked; incorrect or outdated community advice may appear in training examples
+- Only ~40 examples (~20%) were manually reviewed for quality before training
+
+**Model behaviour limitations:**
+- The model hallucinates specific facts (prices, distances, operating hours, visa fees) when retrieved context does not provide explicit grounding
+- It does not reliably abstain from answering when it does not know — it tends to produce a confident-sounding response regardless
+- Response consistency is variable; the same query may produce meaningfully different answers across runs
+- The model may reflect biases present in Reddit data, including opinions presented as facts
+
+### Recommendations
+
+- Always pair this model with a retrieval pipeline for factual queries
+- Display a disclaimer to end users that outputs may be inaccurate
+- Do not deploy in contexts where incorrect travel information could cause harm
+- Treat all specific factual claims (prices, hours, requirements) as unverified until confirmed from an authoritative source
+
+---
+
+## How to Get Started with the Model
+
+### Basic inference (no RAG)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "yowww1094/tourism-assistant-qwen2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful tourism assistant. Answer travel questions clearly and concisely."},
+    {"role": "user", "content": "What is the best time of year to visit Morocco?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+        repetition_penalty=1.1
+    )
+
+response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+```
+
+### Recommended inference (with RAG context)
+
+```python
+# retrieved_context = top-k chunks from your Qdrant vector store
+
+system_prompt = "You are a helpful tourism assistant. Use only the provided context to answer. If the context does not contain enough information, say so."
+
+user_prompt = f"""Context:
+{retrieved_context}
+
+Question: {user_query}"""
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt}
+]
+
+# then apply chat template and generate as above
+```
+
+---
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on [yowww1094/tourism-llm-fine-tuning-dataset](https://huggingface.co/datasets/yowww1094/tourism-llm-fine-tuning-dataset), a custom dataset generated through the following pipeline:
+
+1. Reddit travel posts and comments were scraped from subreddits including r/travel, r/Morocco, r/solotravel, and r/backpacking using PRAW
+2. Raw text was cleaned (length filter, deduplication, HTML stripping, encoding fix) and chunked
+3. Chunks were embedded with `all-MiniLM-L6-v2` and indexed in a Qdrant vector store
+4. A locally-hosted Qwen2.5-7B (via Ollama) was prompted to generate grounded question-answer pairs from retrieved context chunks
+5. Outputs were post-processed into ChatML format and manually sampled for quality (~40 examples reviewed)
+
+**Dataset size:** ~150–200 question-answer pairs  
+**Format:** JSON Lines  
+**Split:** 90% train / 10% validation  
+**Quality note:** Only ~65% of manually reviewed examples were rated acceptable. No automated quality filter was applied to the full dataset.
+
+### Training Procedure
+
+#### Preprocessing
+
+Each example was formatted using the Qwen2.5-Instruct chat template via `tokenizer.apply_chat_template()`. Sequences were truncated to a maximum length of 512 tokens. No data augmentation was applied.
+
+#### Training Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Training regime | FP16 mixed precision |
+| LoRA rank (r) | 16 |
+| LoRA alpha | 32 |
+| LoRA dropout | 0.05 |
+| Target modules | q_proj, v_proj |
+| Epochs | 3 |
+| Batch size | 4 |
+| Gradient accumulation steps | 4 (effective batch size: 16) |
+| Learning rate | 2e-4 |
+| LR scheduler | Cosine annealing |
+| Warmup ratio | 0.03 |
+| Max sequence length | 512 tokens |
+| Optimizer | AdamW (default via transformers) |
+
+#### Hardware and Duration
+
+- **Hardware:** NVIDIA T4 GPU (15 GB VRAM) — Kaggle free tier
+- **Training time:** ~45–60 minutes for 3 epochs on ~150 examples
+- **Framework:** transformers 4.x + peft + trl (SFTTrainer) + accelerate
+
+---
+
+## Evaluation
+
+### Testing Data
+
+Evaluation was performed on a held-out validation split of ~15–20 examples (10% of the total dataset). Due to the small size of this split, quantitative metrics should be interpreted with significant caution — they are not statistically reliable estimates of generalisation performance.
+
+In addition to automatic metrics, a qualitative evaluation was performed by manually inspecting model outputs on ~20 held-out queries not present in the training data.
+
+### Metrics
+
+| Metric | Value | Notes |
+|---|---|---|
+| Training loss (epoch 1) | ~1.62 | |
+| Training loss (epoch 3) | ~0.85 | Consistent decrease across epochs |
+| Validation loss (epoch 1) | ~1.74 | |
+| Validation loss (epoch 3) | ~1.29 | Slight divergence from train loss — mild overfitting |
+| Validation perplexity | ~2.3 | On 15–20 examples only; not a reliable generalisation estimate |
+
+### Results
+
+**What improved after fine-tuning:**
+- Response style and tone became more focused and consistently helpful compared to the base model
+- The model more readily uses tourism-relevant vocabulary and response structure
+- For in-distribution queries (topics well-represented in the dataset), the combination of fine-tuning + RAG outperforms either mechanism alone
+
+**What did not improve:**
+- Factual accuracy on out-of-distribution queries is comparable to the base model — fine-tuning at this scale does not inject meaningful new factual knowledge
+- Hallucination rate on specific facts (prices, dates, requirements) is unchanged without retrieval grounding
+- The model occasionally produces responses that closely paraphrase training examples, suggesting partial memorisation
+
+#### Summary
+
+Fine-tuning on this small dataset improved behavioral style but not factual coverage. The model is more useful when paired with a retrieval pipeline than when used standalone. A dataset 10–20× larger with verified factual content would be needed to produce a genuinely reliable tourism assistant.
+
+---
+
+## Environmental Impact
+
+Carbon emissions were not formally measured. Estimated figures based on training setup:
+
+- **Hardware type:** NVIDIA T4 GPU (Kaggle free tier)
+- **Hours used:** ~1 hour total training time
+- **Cloud provider:** Google (Kaggle infrastructure)
+- **Compute region:** Unknown
+- **Carbon emitted:** Estimated < 0.05 kg CO₂eq (based on [ML Impact Calculator](https://mlco2.github.io/impact#compute))
+
+---
+
+## Technical Specifications
+
+### Model Architecture
+
+- **Architecture:** Qwen2.5 decoder-only transformer (causal LM)
+- **Parameters:** 1.5 billion (base model)
+- **Context window:** 32,768 tokens (base model capability; fine-tuning used max 512 tokens)
+- **Fine-tuning method:** LoRA adapters applied to `q_proj` and `v_proj` attention matrices, then merged into base weights before upload
+
+### Compute Infrastructure
+
+- **Training:** Kaggle free-tier notebook, T4 GPU, 15 GB VRAM
+- **Inference:** Hugging Face Inference Endpoints (free tier)
+- **Vector store:** Qdrant Cloud (free tier, ~1,200 indexed chunks)
+- **Embedding:** `sentence-transformers/all-MiniLM-L6-v2`, CPU inference
+
+### Software
+
+```
+transformers>=4.40.0
+peft>=0.10.0
+trl>=0.8.0
+accelerate>=0.28.0
+sentence-transformers>=2.6.0
+qdrant-client>=1.9.0
+torch>=2.1.0
+```
+
+---
+
+## Citation
+
+If you reference this project, please cite it as:
+
+```bibtex
+@misc{younes2026tourismllm,
+  author    = {Younes},
+  title     = {End-to-End LLM Pipeline for Tourism Assistant},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {[Model](https://huggingface.co/yowww1094/tourism-llm-fine-tuned-qwen2-1.5b-lora-merged)}
+}
+```
+
+---
+
+## More Information
+
+Full technical report (covering pipeline design, training decisions, limitations, and results in detail):
+[docs/technical_report.pdf](https://github.com/yowww1094/llm-tourism-assistant/blob/master/docs/technical_report.pdf)
+
+GitHub repository with full pipeline code:
+[Github](https://github.com/yowww1094/llm-tourism-assistant)
+
+Training dataset:
+[yowww1094/tourism-llm-fine-tuning-dataset](https://huggingface.co/datasets/yowww1094/tourism-llm-fine-tuning-dataset)
+
+---
+
+## Model Card Author
+
+Younes AIT SI ABBOU — personal learning project, April 2026

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