Instructions to use TilQazyna/Til-2B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TilQazyna/Til-2B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="TilQazyna/Til-2B-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TilQazyna/Til-2B-instruct")
model = AutoModelForCausalLM.from_pretrained("TilQazyna/Til-2B-instruct")
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 TilQazyna/Til-2B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TilQazyna/Til-2B-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": "TilQazyna/Til-2B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TilQazyna/Til-2B-instruct

SGLang

How to use TilQazyna/Til-2B-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-2B-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": "TilQazyna/Til-2B-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 "TilQazyna/Til-2B-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": "TilQazyna/Til-2B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TilQazyna/Til-2B-instruct with Docker Model Runner:
```
docker model run hf.co/TilQazyna/Til-2B-instruct
```

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Til-2B-instruct

Til-2B-instruct — Til-2B base-моделінің нұсқаулықты орындайтын (instruction-tuned) нұсқасы. Қазақша сұрақ-жауап, мәтін жазу, қорытындылау, қайта жазу сияқты тапсырмаларды орындайды.

Til-2B-instruct is an instruction-tuned version of the Til-2B base model, for Kazakh-first chat, question answering, writing, summarization and rewriting.

Model details


Base	TilQazyna/Til-2B (1977M, dense + MLA)
Format	ChatML (`<
Loss	assistant tokens only
Data	~345K instruction–response pairs (≈70% Kazakh; also ru/en/code/math)
Epochs / LR	3 / 1e-5 (cosine), bf16
Context	4096
Hardware	8×H200, DDP

The instruction mix covers QA, summarization, rephrasing, classification, NER, keyword extraction, simplification, title generation and general chat, with Russian/English/code/math instructions for cross-lingual robustness.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "TilQazyna/Til-2B-instruct"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")

msgs = [{"role": "user", "content": "Денсаулықты сақтау үшін 3 кеңес бер."}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
ids = tok(text, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
out = model.generate(ids, max_new_tokens=300, do_sample=True, temperature=0.7,
                     top_p=0.9, repetition_penalty=1.1, pad_token_id=0,
                     eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"))
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Sample outputs

Q: Абай Құнанбайұлы кім болған? A: Абай Құнанбайұлы — қазақ халқының ұлы ақыны, ойшыл және қоғам қайраткері. Ол қазіргі Қазақстан аумағында дүниеге келген, өмірінің көп бөлігін Семей қаласында өткізген… (өлеңдері мен қара сөздері туралы жалғасады).

Q: Денсаулықты сақтау үшін 3 кеңес бер. A: 1. Көбірек су ішіңіз… 2. Дұрыс тамақтаныңыз… 3. Темекіден бас тартыңыз…

Intended use & limitations

Intended: Kazakh-first assistant for QA, writing, summarization, rewriting.
Reasoning/math: arithmetic and multi-step reasoning are weak (a known limit at this scale); the model may switch to Russian on math prompts.
Factuality: can hallucinate; verify facts and numbers.
No safety alignment / RLHF has been applied.

License

Apache 2.0. Access is gated (manual approval) for usage tracking.

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Safetensors

Model size

2B params

Tensor type

BF16

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