Instructions to use TilQazyna/Til-Core-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TilQazyna/Til-Core-0.5B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TilQazyna/Til-Core-0.5B-Instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("TilQazyna/Til-Core-0.5B-Instruct") model = AutoModelForMultimodalLM.from_pretrained("TilQazyna/Til-Core-0.5B-Instruct") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TilQazyna/Til-Core-0.5B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TilQazyna/Til-Core-0.5B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TilQazyna/Til-Core-0.5B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TilQazyna/Til-Core-0.5B-Instruct
- SGLang
How to use TilQazyna/Til-Core-0.5B-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 "TilQazyna/Til-Core-0.5B-Instruct" \ --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": "TilQazyna/Til-Core-0.5B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "TilQazyna/Til-Core-0.5B-Instruct" \ --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": "TilQazyna/Til-Core-0.5B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TilQazyna/Til-Core-0.5B-Instruct with Docker Model Runner:
docker model run hf.co/TilQazyna/Til-Core-0.5B-Instruct
Til Core 0.5B Instruct
Til Core 0.5B Instruct is the instruction-tuned variant of Til Core 0.5B — a 498M-parameter Qwen2 Kazakh model with a 256K morpheme-aware vocabulary. It was supervised-fine-tuned (SFT) on Kazakh instruction data to follow instructions in Kazakh.
Experiment
exp053. Part of the Til Core program (TilQazyna).
Training (SFT)
| Base | TilQazyna/Til-Core-0.5B (from-scratch, 256K morphbpe) |
| Data | AmanMussa/kazakh-instruction-v2 — 52,201 Kazakh instruction examples (Alpaca format) |
| Format | Alpaca (Kazakh template), completion-only loss masking |
| Epochs | 3 (612 steps, global batch 256) |
| Optimizer | AdamW, lr 2e-5 cosine, warmup 3%, wd 0.01, grad-clip 1.0, bf16 |
| Hardware | 8 × NVIDIA H200 |
| Final train loss | 3.12 |
Prompt format (Alpaca, Kazakh)
### Нұсқаулық:
{instruction}
### Жауап:
or with input:
### Нұсқаулық:
{instruction}
### Кіріс:
{input}
### Жауап:
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "TilQazyna/Til-Core-0.5B-Instruct"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto").eval()
prompt = "### Нұсқаулық:\nДені сау болу үшін үш кеңес беріңіз.\n\n### Жауап:\n"
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=120, do_sample=True,
temperature=0.7, top_p=0.9, repetition_penalty=1.2)
print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))
Examples
Real generations (temperature 0.7, top_p 0.9, repetition_penalty 1.2):
Tips (list following):
### Нұсқаулық: Дені сау болу үшін үш кеңес беріңіз.
→ 1. Үнемі жаттығу жасаңыз және сіздің денеңізді босаңсытыңыз.
2. Қажетсіз, тым көп тамақ жеуге болмайды.
3. Тұрақты жаттығулар жасауды бастаңыз және үнемі үзіліс жасаңыз.
Short writing:
### Нұсқаулық: Көктем мезгілі туралы қысқаша жаз.
→ Көктемде жердің жылуы, құстардың үні және адамдар өздерінің ерекше
мерекесі үшін қуанды. Олар жаңбыр жауып, күн батқанда, өздерімен
бірге жаңа жапырақтар мен гүлдерді әкелді.
Explanation:
### Нұсқаулық: Достық деген не? Қысқаша түсіндір.
→ Дос — бұл достарына, олармен бірге жаңа адамдардың пайда болуына
және олардың арасындағы қарым-қатынасты дамытуға мүмкіндік беретін
ерекше тұлға. Ол әрқашан бір-бірінің достығын қолдайды...
The model reliably follows the format and writes fluent Kazakh; it is weaker on precise/factual tasks (see Limitations).
Limitations
- 498M params — follows instructions and writes fluent Kazakh, but factual accuracy is weak (it can hallucinate facts, e.g. wrong capitals). Use for fluency / format / as an SFT baseline, not as a knowledge source.
- SFT only (no RLHF/DPO); no safety alignment.
- Single-turn Alpaca format; not a multi-turn chat model.
Reproducibility
seed 42 · base TilQazyna/Til-Core-0.5B · data AmanMussa/kazakh-instruction-v2@c641407 · transformers==5.10.2, torch==2.11.0+cu128 · config exp053 · torchrun --nproc_per_node=8 -m slm.train_sft --config configs/experiments/exp053_sft_til_core_05b.yaml.
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