Instructions to use sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct")
model = AutoModelForCausalLM.from_pretrained("sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct

SGLang

How to use sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct with Docker Model Runner:
```
docker model run hf.co/sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct
```

TSFT-RAG Qwen2.5-1.5B-Instruct

Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation

TSFT-RAG Qwen2.5-1.5B-Instruct is a full-parameter fine-tuned derivative of Qwen/Qwen2.5-1.5B-Instruct.

Part of the TSFT-RAG model family for Retrieval-Augmented Generation across Gemma, Qwen and Llama architectures.

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.

TSFT-RAG Model Series

Model	Parameters	Status
Qwen2.5-0.5B-Instruct	0.5B	Released
Qwen2.5-1.5B-Instruct	1.5B	Released
Qwen2.5-7B-Instruct	7B	Released
Qwen2.5-14B-Instruct	14B	Released

The model was trained to improve several behaviours that are important in practical RAG systems:

answering from supplied context;
abstaining when the context does not support an answer;
following task-specific output requirements;
producing structured JSON output;
generating source-aware and citation-oriented responses.

The main observed gains are in hard-negative handling, structured-output validity, and citation-related behaviour. Performance does not improve uniformly across all metrics; in particular, the measured grounded-QA score is lower than that of the unchanged base model in the reported evaluation.

Model Details

Property	Value
Model family	TSFT-RAG
Base model	Qwen/Qwen2.5-1.5B-Instruct
Architecture	Qwen2ForCausalLM
Parameters	approximately 1.5B
Training method	full-parameter supervised fine-tuning
Primary language of the training corpus	German
Context length supported by the configuration	32,768 tokens
Weight dtype	bfloat16
Framework	Transformers
License	Apache-2.0

Intended Use

This model is intended for research and experimental use in RAG-oriented workflows.

Suitable use cases include:

context-grounded question answering;
evaluation of abstention behaviour;
generation of structured RAG responses;
source-aware answer generation;
comparison of base and fully fine-tuned small language models;
local or resource-constrained RAG experiments.

The model should be used with retrieved evidence or other explicitly supplied context. It is not intended to serve as a standalone factual knowledge source.

Training Data

The model was trained on a fixed, task-specific German-language corpus designed for RAG-oriented instruction following.

The training examples cover:

answerable context-grounded questions;
hard-negative and unanswerable questions;
multiple assistant roles and response styles;
analysis tasks for main topic, key message, and main arguments;
JSON and short-answer output formats;
citation-aware responses.

The complete raw training corpus is not distributed with this checkpoint. The accompanying repository documents the data-processing, training, and evaluation pipeline.

Training Procedure

This checkpoint was produced using full-parameter supervised fine-tuning rather than LoRA, QLoRA, or another adapter-based method.

Training argument	Value
Epochs	3
Learning rate	1e-6
Per-device train batch size	1
Per-device evaluation batch size	1
Gradient accumulation steps	16
Effective batch size per process	16
Optimizer	paged AdamW 8-bit
Scheduler	cosine
Warmup ratio	0.08
Weight decay	0.01
Maximum gradient norm	1.0
Precision	bfloat16
FP16	disabled
Packing	disabled
Seed	42

The recorded training runtime was approximately 21,789 seconds (about 6 h 3 min), with a final reported training loss of 1.0738.

Evaluation

The fine-tuned model and the unchanged base model were evaluated on the same held-out test set of 1,940 instances.

Metric	Base model	TSFT-RAG	Difference
Aggregate score	0.3321	0.4308	+0.0987
Hard-negative handling	0.0056	0.3418	+0.3362
Grounded QA	0.5289	0.4803	-0.0486
Analysis: main topic	0.5593	0.5518	-0.0075
Analysis: key message	0.5210	0.4810	-0.0400
Analysis: main arguments	0.4549	0.3998	-0.0551
JSON validity	0.7477	0.8862	+0.1386
Citation precision	0.0000	0.6502	+0.6502
Citation recall	0.0000	0.6502	+0.6502

The strongest change is in hard-negative handling: the unchanged base model correctly rejected 4 of the 708 hard-negative cases under the benchmark criterion, while the fine-tuned model correctly rejected 242. JSON validity and citation-related scores also increased substantially.

These results should be interpreted as task-specific benchmark outcomes rather than general-purpose capability claims. The model improves substantially in abstention, JSON validity, and citation-related behaviour, while grounded-QA and the three analysis-task scores decrease relative to the unchanged base model.

Further evaluation details, scripts, and analysis are available in the accompanying repository and paper.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "Du bist ein sachlicher RAG-Assistent. "
            "Beantworte die Frage präzise nur auf Grundlage des Kontexts. "
            "Wenn der Kontext die Antwort nicht belegt, sage dies ausdrücklich."
        ),
    },
    {
        "role": "user",
        "content": (
            "Kontext:\n"
            "Ein Retrieval-Modul liefert relevante Dokumentpassagen an das Sprachmodell.\n\n"
            "Frage:\n"
            "Welche Komponente stellt dem Sprachmodell die Dokumentpassagen bereit?"
        ),
    },
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
    )

generated_tokens = output[0, inputs["input_ids"].shape[1]:]

print(
    tokenizer.decode(
        generated_tokens,
        skip_special_tokens=True,
    )
)

Prompting Notes

The model was trained with Qwen's chat template and should be used through tokenizer.apply_chat_template(...).

For RAG use, prompts should clearly separate:

the behavioural instruction;
the retrieved context;
the user question;
any required output format.

For deterministic evaluation, use greedy decoding or another fixed decoding setup.

Limitations

The model may produce incorrect, incomplete, or unsupported answers.
Fine-tuning does not guarantee faithful use of retrieved evidence.
Retrieval quality remains a major determinant of answer quality.
The model may fail to abstain even when evidence is insufficient.
Citation-like output does not guarantee that a cited passage truly supports a claim.
The 1.5B parameter scale still limits reasoning depth and general robustness.
Performance outside the evaluated RAG task formats has not been systematically established.
The training corpus is primarily German, so behaviour may differ in other languages.
The model inherits limitations and biases from the Qwen2.5 base model and from the task-specific training data.
The observed reductions in grounded-QA and analysis-task scores indicate real trade-offs and should not be ignored when selecting the model for deployment.

The model should not be used for high-stakes medical, legal, financial, safety-critical, or administrative decisions without independent validation and human oversight.

Ethical Considerations

Retrieved documents may contain inaccurate, biased, confidential, or outdated information. Such content can be reproduced or amplified by the model.

Users are responsible for:

validating retrieval sources;
protecting confidential material;
testing model behaviour in the intended domain;
monitoring unsupported or misleading output;
complying with applicable law, policy, and licensing requirements.

Relationship to the Research Project

This checkpoint is one model from a controlled cross-family study of task-specific full fine-tuning for RAG. The study compares base and fine-tuned configurations from the Gemma, Llama, and Qwen model families across parameter scales from approximately 0.5B to 14B.

Code, training scripts, evaluation scripts, and documentation:

https://github.com/frankmst/rag-task-specific-full-finetuning

Base Model and License

This model is derived from Qwen/Qwen2.5-1.5B-Instruct, which is distributed under the Apache License 2.0.

This derivative checkpoint is also released under the Apache License 2.0. Users should review the license file included in this repository and the licensing information of the base model.

Associated Publication

This model was developed and evaluated as part of the following research:

Frank, S., & Singh, R. (2026). Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation: A Multi-Family Evaluation Across Open-Weight Language Models.

Zenodo. DOI: 10.5281/zenodo.21638352

If you use this model in academic work, please cite the publication above.

@misc{Frank2026TSFTRAG,
  author       = {Frank, Sascha and Singh, Rawel},
  title        = {Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation:
                  A Multi-Family Evaluation Across Open-Weight Language Models},
  year         = {2026},
  month        = jul,
  version      = {v1},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21638352},
  url          = {https://doi.org/10.5281/zenodo.21638352},
  note         = {Preprint}
}