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Model: sascha-frank-ai-research/tsft-rag-qwen2.5-0.5b-instruct Source: Original Platform
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README.md
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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base_model_relation: finetune
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language:
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- de
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- en
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tags:
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- rag
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- retrieval-augmented-generation
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- task-specific-fine-tuning
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- full-fine-tuning
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- qwen2.5
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- text-generation
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- structured-output
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- abstention
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- research
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- tsft-rag
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- model-card-v1
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---
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# TSFT-RAG Qwen2.5-0.5B-Instruct
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**Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation**
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TSFT-RAG Qwen2.5-0.5B-Instruct is a full-parameter fine-tuned derivative of
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[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
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Part of the [TSFT-RAG model family](https://huggingface.co/sascha-frank-ai-research) for Retrieval-Augmented Generation across Gemma, Qwen and Llama architectures.
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The objective is to investigate how supervised full fine-tuning changes the behaviour of modern language models when used as Retrieval-Augmented Generation (RAG) systems.
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---
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# TSFT-RAG Model Series
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| Model | Parameters | Status |
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|--------|-----------:|--------|
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| **Qwen2.5-0.5B-Instruct** | **0.5B** | **Released** |
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| [Qwen2.5-1.5B-Instruct](https://huggingface.co/sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct) | 1.5B | Released |
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| [Qwen2.5-7B-Instruct](https://huggingface.co/sascha-frank-ai-research/tsft-rag-qwen2.5-7b-instruct) | 7B | Released |
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| [Qwen2.5-14B-Instruct](https://huggingface.co/sascha-frank-ai-research/tsft-rag-qwen2.5-14b-instruct) | 14B | Released |
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---
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The model was trained to improve several behaviours that are important in practical
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RAG systems:
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- answering from supplied context;
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- abstaining when the context does not support an answer;
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- following task-specific output requirements;
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- producing structured JSON output;
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- generating source-aware and citation-oriented responses.
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The main observed gains are in hard-negative handling, structured-output validity,
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and citation-related behaviour. Performance does not improve uniformly across all
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metrics; in particular, the measured grounded-QA score is lower than that of the
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unchanged base model in the reported evaluation.
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## Model Details
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| Property | Value |
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|---|---|
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| Model family | TSFT-RAG |
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| Base model | Qwen/Qwen2.5-0.5B-Instruct |
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| Architecture | Qwen2ForCausalLM |
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| Parameters | approximately 0.5B |
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| Training method | full-parameter supervised fine-tuning |
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| Primary language of the training corpus | German |
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| Context length supported by the configuration | 32,768 tokens |
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| Weight dtype | bfloat16 |
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| Framework | Transformers |
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| License | Apache-2.0 |
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## Intended Use
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This model is intended for research and experimental use in RAG-oriented workflows.
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Suitable use cases include:
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- context-grounded question answering;
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- evaluation of abstention behaviour;
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- generation of structured RAG responses;
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- source-aware answer generation;
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- comparison of base and fully fine-tuned small language models;
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- local or resource-constrained RAG experiments.
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The model should be used with retrieved evidence or other explicitly supplied context.
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It is not intended to serve as a standalone factual knowledge source.
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## Training Data
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The model was trained on a fixed, task-specific German-language corpus designed for
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RAG-oriented instruction following.
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The training examples cover:
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- answerable context-grounded questions;
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- hard-negative and unanswerable questions;
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- multiple assistant roles and response styles;
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- analysis tasks for main topic, key message, and main arguments;
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- JSON and short-answer output formats;
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- citation-aware responses.
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The complete raw training corpus is not distributed with this checkpoint. The
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accompanying repository documents the data-processing, training, and evaluation
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pipeline.
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## Training Procedure
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This checkpoint was produced using full-parameter supervised fine-tuning rather than
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LoRA, QLoRA, or another adapter-based method.
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| Training argument | Value |
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|---|---:|
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| Epochs | 3 |
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| Learning rate | 1e-6 |
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| Per-device train batch size | 1 |
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| Per-device evaluation batch size | 1 |
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| Gradient accumulation steps | 16 |
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| Effective batch size per process | 16 |
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| Optimizer | paged AdamW 8-bit |
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| Scheduler | cosine |
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| Warmup ratio | 0.08 |
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| Weight decay | 0.01 |
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| Maximum gradient norm | 1.0 |
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| Precision | bfloat16 |
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| FP16 | disabled |
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| Packing | disabled |
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| Seed | 42 |
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The recorded training runtime was approximately **17,266 seconds** (about **4 h 48 min**),
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with a final reported training loss of **1.3059**.
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## Evaluation
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The fine-tuned model and the unchanged base model were evaluated on the same held-out
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test set of **1,940 instances**.
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| Metric | Base model | TSFT-RAG | Difference |
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|---|---:|---:|---:|
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| Aggregate score | 0.2456 | 0.3546 | +0.1091 |
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| Hard-negative handling | 0.0000 | 0.2938 | +0.2938 |
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| Grounded QA | 0.4406 | 0.3811 | -0.0595 |
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| Analysis: main topic | 0.3683 | 0.3812 | +0.0129 |
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| Analysis: key message | 0.4068 | 0.4774 | +0.0705 |
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| Analysis: main arguments | 0.2992 | 0.3153 | +0.0162 |
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The strongest change is in hard-negative handling: the base model failed all 708
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hard-negative cases under the benchmark criterion, while the fine-tuned model
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successfully rejected 208 of them.
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These results should be interpreted as task-specific benchmark outcomes rather than
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general-purpose capability claims. The model improves substantially on some trained
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behaviours while showing a trade-off on grounded-QA performance.
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Further evaluation details, scripts, and analysis are available in the accompanying
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repository and paper.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "sascha-frank-ai-research/tsft-rag-qwen2.5-0.5b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{
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"role": "system",
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"content": (
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"Du bist ein sachlicher RAG-Assistent. "
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"Beantworte die Frage präzise nur auf Grundlage des Kontexts. "
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"Wenn der Kontext die Antwort nicht belegt, sage dies ausdrücklich."
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),
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},
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{
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"role": "user",
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"content": (
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"Kontext:\n"
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"Ein Retrieval-Modul liefert relevante Dokumentpassagen an das Sprachmodell.\n\n"
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"Frage:\n"
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"Welche Komponente stellt dem Sprachmodell die Dokumentpassagen bereit?"
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),
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},
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.inference_mode():
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output = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=False,
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)
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generated_tokens = output[0, inputs["input_ids"].shape[1]:]
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print(
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tokenizer.decode(
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generated_tokens,
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skip_special_tokens=True,
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)
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)
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```
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## Prompting Notes
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The model was trained with Qwen's chat template and should be used through
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`tokenizer.apply_chat_template(...)`.
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For RAG use, prompts should clearly separate:
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1. the behavioural instruction;
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2. the retrieved context;
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3. the user question;
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4. any required output format.
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For deterministic evaluation, use greedy decoding or another fixed decoding setup.
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## Limitations
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- The model may produce incorrect, incomplete, or unsupported answers.
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- Fine-tuning does not guarantee faithful use of retrieved evidence.
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- Retrieval quality remains a major determinant of answer quality.
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- The model may fail to abstain even when evidence is insufficient.
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- Citation-like output does not guarantee that a cited passage truly supports a claim.
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- The 0.5B parameter scale limits reasoning depth and general robustness.
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- Performance outside the evaluated RAG task formats has not been systematically established.
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- The training corpus is primarily German, so behaviour may differ in other languages.
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- The model inherits limitations and biases from the Qwen2.5 base model and from the
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task-specific training data.
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- The observed reduction in grounded-QA score indicates a real trade-off and should not
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be ignored when selecting the model for deployment.
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The model should not be used for high-stakes medical, legal, financial, safety-critical,
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or administrative decisions without independent validation and human oversight.
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## Ethical Considerations
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Retrieved documents may contain inaccurate, biased, confidential, or outdated
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information. Such content can be reproduced or amplified by the model.
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Users are responsible for:
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- validating retrieval sources;
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- protecting confidential material;
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- testing model behaviour in the intended domain;
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- monitoring unsupported or misleading output;
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- complying with applicable law, policy, and licensing requirements.
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## Relationship to the Research Project
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This checkpoint is one model from a controlled cross-family study of task-specific
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full fine-tuning for RAG. The study compares base and fine-tuned configurations from
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the Gemma, Llama, and Qwen model families across parameter scales from approximately
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0.5B to 14B.
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Code, training scripts, evaluation scripts, and documentation:
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https://github.com/frankmst/rag-task-specific-full-finetuning
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## Base Model and License
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This model is derived from
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[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct),
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which is distributed under the Apache License 2.0.
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This derivative checkpoint is also released under the Apache License 2.0. Users
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should review the license file included in this repository and the licensing
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information of the base model.
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## Citation
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A formal paper citation will be added after publication of the accompanying paper.
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Until then, please cite the software repository:
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```bibtex
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@software{frank_tsft_rag_2026,
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author = {Sascha Frank},
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title = {Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation},
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year = {2026},
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url = {https://github.com/frankmst/rag-task-specific-full-finetuning}
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}
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```
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## Project
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The complete TSFT-RAG research project, including training scripts,
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evaluation pipeline, benchmark methodology and documentation, is available at:
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**Project repository**
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https://github.com/frankmst/rag-task-specific-full-finetuning
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**TSFT-RAG model family**
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https://huggingface.co/sascha-frank-ai-research
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---
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## Author
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**Sascha Frank**
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Independent AI Researcher
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**ORCID**
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https://orcid.org/0000-0002-0588-0081
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**GitHub**
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https://github.com/frankmst
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**Hugging Face**
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https://huggingface.co/sascha-frank-ai-research
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## Links
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- Project repository: https://github.com/frankmst/rag-task-specific-full-finetuning
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- Base model: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct
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- Project website: https://sascha-frank.com
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- Paper: forthcoming
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