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Note: TJ-1.0 is available via API only and is not available for download or local deployment.
| Property | Value |
|---|---|
| Developer | SoulLab |
| Model type | Instruction-tuned Causal Language Model |
| Architecture | Decoder-only Transformer with Grouped Query Attention (GQA) |
| Positional Encoding | Rotary Position Embedding (RoPE) |
| Tokenizer | Byte-Pair Encoding (BPE), extended vocabulary for Tajik Cyrillic & Latin |
| Fine-tuning | Supervised Fine-Tuning (SFT) + RLHF (Reinforcement Learning from Human Feedback) |
| Context window | 128,000 tokens |
| Max output tokens | 8,192 tokens |
| Knowledge cutoff | Q3 2024 |
| Languages | Tajik (tg), Russian (ru), English (en), and 50+ languages |
| License | Proprietary — TajikGPT Terms |
| Training hardware | NVIDIA A100 80GB, bf16 precision, PyTorch |
| Source Category | Description | Approx. Share |
|---|---|---|
| Tajik Web Corpus | News, blogs, forums, government portals in Tajik (Cyrillic & Latin) | 28% |
| Tajik Literature & Culture | Books, poetry, historical texts, folklore | 12% |
| Tajik Legislation | Laws, decrees, official government documents | 8% |
| Multilingual Web | High-quality filtered web data (Russian, English, and others) | 32% |
| Instruction & Dialogue | Human-written and synthetic instruction-following data | 14% |
| Code | Source code across major programming languages | 6% |
| Benchmark | Score | # Shots | Metric |
|---|---|---|---|
| MMLU (Massive Multitask Language Understanding) | 72.1% | 5-shot | Accuracy |
| MT-Bench (Multi-turn instruction following) | 7.1 / 10 | 0-shot | GPT-4 Judge |
| HumanEval (Code generation) | 58.3% | 0-shot | pass@1 |
| HellaSwag (Commonsense reasoning) | 81.4% | 10-shot | Accuracy |
These are the first published benchmarks for Tajik-language LLM evaluation.
| Benchmark | Score | Description |
|---|---|---|
| TajikQA | 78.4% | Open-domain Q&A in Tajik language |
| TajikTranslate | 81.2% BLEU | Tajik ↔ Russian translation |
| TajikInstruct | 74.6% | Instruction following in Tajik |
pip install tajikgpt1from tajikgpt import TajikGPT
2
3client = TajikGPT(api_key="sk-tj-your-key")
4
5response = client.chat.completions.create(
6 model="tj-1.0",
7 messages=[
8 {"role": "system", "content": "Ты полезный помощник."},
9 {"role": "user", "content": "Ба забони тоҷикӣ шарҳ деҳ: нейронӣ шабака чист?"}
10 ]
11)
12print(response.choices[0].message.content)1curl -X POST https://tajikgpt.com/api/tj/chat \
2 -H "Content-Type: application/json" \
3 -H "Authorization: Bearer sk-tj-your-key" \
4 -d '{
5 "model": "tj-1.0",
6 "messages": [
7 {"role": "user", "content": "Hello! What can you do?"}
8 ],
9 "max_tokens": 1024,
10 "temperature": 0.7
11 }'| Model | Context | Max Output | Specialty | Tier |
|---|---|---|---|---|
| TJ-1.0 Mini | 128K | 4,096 | Fast & lightweight | Free |
| TJ-1.0 | 128K | 8,192 | Balanced — general purpose | Free |
| TJ-1.0 Pro | 128K | 16,384 | Advanced + Vision | Plus |
| TJ-1.0 Ultra | 128K | 32,768 | Top performance | Plus |
| TJ-Coder | 131K | 32,768 | Code specialist | Free |
| TJ-Image 1.0 | — | — | Text-to-Image | Free |
1@misc{tajikgpt2024tj10,
2 title = {TJ-1.0: A Multilingual Large Language Model with Native Tajik Language Support},
3 author = {SoulLab},
4 year = {2024},
5 howpublished = {\url{https://tajikgpt.com}},
6 note = {Proprietary model, available via API at https://tajikgpt.com}
7}