klusai/ds-tf2-en-ro-15k
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How to use klusai/tf3-26m-student with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="klusai/tf3-26m-student") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("klusai/tf3-26m-student")
model = AutoModelForCausalLM.from_pretrained("klusai/tf3-26m-student")How to use klusai/tf3-26m-student with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "klusai/tf3-26m-student"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "klusai/tf3-26m-student",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/klusai/tf3-26m-student
How to use klusai/tf3-26m-student with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "klusai/tf3-26m-student" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "klusai/tf3-26m-student",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "klusai/tf3-26m-student" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "klusai/tf3-26m-student",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use klusai/tf3-26m-student with Docker Model Runner:
docker model run hf.co/klusai/tf3-26m-student
A compact 22.9M-parameter Romanian language model distilled from the TF3-50M teacher using logit-based knowledge distillation. Part of the TinyFabulist research project.
| Property | Value |
|---|---|
| Parameters | 22.9M (26.45M with untied embeddings) |
| Architecture | LLaMA-style decoder-only Transformer |
| Hidden size | 384 |
| Attention heads | 6 (head dim 64) |
| Layers | 6 |
| MLP intermediate | 1,024 |
| Vocab size | 32,000 (Unigram, Romanian-specific) |
| Context length | 2,048 tokens |
| Tied embeddings | Yes |
| Training | Knowledge distillation from klusai/tf3-50m-base |
This model is a research artifact demonstrating knowledge distillation for compact Romanian language models trained on synthetic moral microfiction. It is designed for:
Not intended for: Production text generation, factual question answering, or safety-critical applications.
@article{nadas2026tf3,
title={TF3-RO-50M: Training Compact Romanian Language Models from Scratch on Synthetic Moral Microfiction},
author={Nada\c{s}, Mihai Dan and Dio\c{s}an, Laura and Tomescu, Andreea and Pi\c{s}coran, Andrei},
journal={arXiv preprint arXiv:2601.10410},
year={2026}
}
| Artifact | Description |
|---|---|
| klusai/tf3-50m-base | Teacher model (51.65M) |
| klusai/tf3-50m-sft | SFT-tuned teacher |
| klusai/tf3-bert | NER model for entity coherence evaluation |
| klusai/ds-tf2-en-ro-3m | 3M bilingual fable corpus |
| klusai/ds-tf2-en-ro-15k | 15k curated subset for distillation/SFT |
Base model
klusai/tf3-50m-base