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Model: varunchundru/dpo-qwen2.5-0.5b-halueval Source: Original Platform
2026-06-16 05:42:16 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+language:
+- en
+tags:
+- hallucination-reduction
+- dpo
+- direct-preference-optimization
+- qwen2
+- halueval
+- nlp
+- trl
+pipeline_tag: text-generation
+datasets:
+- pminervini/HaluEval
+---
+
+# dpo-qwen2.5-0.5b-halueval
+
+A **Direct Preference Optimization (DPO)** fine-tune of [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) trained to reduce hallucination across grounded QA, dialogue, and summarization tasks. Fine-tuned on preference pairs derived from all three subtasks of the [HaluEval](https://huggingface.co/datasets/pminervini/HaluEval) benchmark.
+
+This model is the **mitigation component** of a complete hallucination detection + mitigation pipeline built for CS 593 NLP (Purdue University Fort Wayne, Spring 2026). Hallucination rates are measured by passing model generations through a separately fine-tuned DeBERTa detector: [`varunchundru/hallucination-detector-deberta`](https://huggingface.co/varunchundru/hallucination-detector-deberta).
+
+---
+
+## Model Details
+
+| Field | Value |
+|---|---|
+| **Base model** | `Qwen/Qwen2.5-0.5B-Instruct` |
+| **Fine-tuning method** | Direct Preference Optimization (DPO) via TRL |
+| **Training tasks** | QA, Dialogue, Summarization (HaluEval) |
+| **Data split** | 70 / 15 / 15 stratified by task |
+| **Train pairs** | 21,000 (7,000 per task) |
+| **Test samples** | 4,500 (1,500 per task) |
+| **Model size** | 0.5B parameters |
+| **Hardware** | 1× NVIDIA A100 |
+| **Framework** | Hugging Face Transformers + TRL |
+
+---
+
+## Training Details
+
+### Preference Pair Construction
+
+Each HaluEval example was converted into a DPO triplet using task-specific formatting:
+
+| Task | Prompt | Chosen | Rejected |
+|---|---|---|---|
+| **QA** | System + knowledge + question | `right_answer` | `hallucinated_answer` |
+| **Dialogue** | System + knowledge + dialogue history | `right_response` | `hallucinated_response` |
+| **Summarization** | System + document | `right_summary` | `hallucinated_summary` |
+
+**System prompt (QA / Dialogue):**
+```
+You are a helpful assistant. Answer based only on the provided knowledge.
+Do not invent facts. Be concise.
+```
+
+**System prompt (Summarization):**
+```
+Summarize the following document accurately.
+Do not add information not present in the document.
+```
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Epochs | 1 |
+| Learning rate | 5e-6 |
+| DPO beta (KL penalty) | 0.1 |
+| Batch size (train / eval) | 16 |
+| Gradient accumulation steps | 1 |
+| Max sequence length | 512 |
+| Precision | bf16 |
+| Best model selection | Lowest eval loss |
+| Optimizer | AdamW (default TRL) |
+
+---
+
+## Evaluation
+
+Hallucination rates are measured on the held-out 15% test split (4,500 examples, 1,500 per task) by passing each model's generation through [`varunchundru/hallucination-detector-deberta`](https://huggingface.co/varunchundru/hallucination-detector-deberta) at a 0.5 probability threshold.
+
+### Overall Results
+
+| Model | Hallucination Rate | Mean Hall. Prob |
+|---|---|---|
+| Qwen2.5-0.5B-Instruct (base) | 85.5% | 0.816 |
+| **dpo-qwen2.5-0.5b-halueval (this model)** | **37.7%** | **0.293** |
+| Absolute reduction | −47.7 pp | −0.523 |
+| **Relative reduction** | **−55.9%** | |
+
+### Per-Task Breakdown
+
+| Task | Base Rate | DPO Rate | Relative Reduction |
+|---|---|---|---|
+| QA | 93.4% | 19.0% | **−79.7%** |
+| Summarization | 63.2% | 0.0% | **−100.0%** |
+| Dialogue | 99.8% | 94.2% | −5.6% |
+
+**Notable findings:**
+- **QA** sees the largest absolute improvement — the structured knowledge + question format aligns well with DPO training signal.
+- **Summarization** hallucination is effectively eliminated on the test set (0.0% rate), likely because the DPO training directly contrasts faithful vs. hallucinated summaries on similar documents.
+- **Dialogue** shows minimal improvement (−5.6%). The model still hallucinates in 94.2% of dialogue turns, suggesting that multi-turn conversation is a harder distribution to shift with preference learning at this scale.
+
+![Hallucination Rate Reduction](dpo_hallucination_reduction.png)
+
+---
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "varunchundru/dpo-qwen2.5-0.5b-halueval"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model.eval()
+
+
+def answer_grounded(question: str, knowledge: str, max_new_tokens: int = 150) -> str:
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful assistant. Answer based only on the provided knowledge. "
+                "Do not invent facts. Be concise."
+            ),
+        },
+        {
+            "role": "user",
+            "content": f"Knowledge: {knowledge}\n\nQuestion: {question}",
+        },
+    ]
+    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():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=False,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    response = tokenizer.decode(
+        output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
+    )
+    return response.strip()
+
+
+# Example
+knowledge = "The Eiffel Tower is located in Paris, France, and was completed in 1889."
+question = "Where is the Eiffel Tower located and when was it finished?"
+print(answer_grounded(question, knowledge))
+```
+
+---
+
+## Intended Use & Limitations
+
+**Intended use:**
+- Grounded question answering and document summarization where faithfulness to a context is required
+- Research on hallucination mitigation via preference learning
+- Mitigation component paired with [`varunchundru/hallucination-detector-deberta`](https://huggingface.co/varunchundru/hallucination-detector-deberta)
+
+**Limitations:**
+- **Dialogue performance is weak** — DPO training did not meaningfully reduce hallucination for multi-turn dialogue (94.2% post-DPO rate). The dialogue task may require more training, a larger model, or task-specific preference data.
+- Trained for only 1 epoch on 0.5B parameters — further training or a larger base model would likely improve results.
+- Hallucination rates are measured by a proxy DeBERTa classifier, not human annotation.
+- `max_length=512` during DPO training may truncate long documents in the summarization task.
+- Should not be used in high-stakes domains without further validation.
+
+---
+
+## Project Context
+
+This model is the mitigation component in a four-part hallucination pipeline:
+
+1. **TF-IDF + Logistic Regression** — Lightweight lexical baseline
+2. **Zero-Shot DeBERTa-MNLI** — NLI-based detection without task-specific training (Acc=0.60, F1=0.43, AUROC=0.65)
+3. **Fine-Tuned DeBERTa-v3-base** — Task-specific hallucination detector (Acc=0.91, F1=0.91, AUROC=0.98)
+4. **DPO Fine-Tuned Qwen2.5-0.5B (this model)** — Reduces hallucination at generation time
+
+**Authors:** Varun Chundru & Debasmita Biswas
+**Course:** CS 593 Natural Language Processing, Purdue University Fort Wayne, Spring 2026
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{li2023halueval,
+  title={HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models},
+  author={Li, Junyi and Cheng, Xiaoxue and Zhao, Wayne Xin and Nie, Jian-Yun and Wen, Ji-Rong},
+  booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
+  year={2023}
+}
+```
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,57 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.6.2",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,13 @@
+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.6.2"
+}
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--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
--- a/training_args.bin
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