Instructions to use nur-dev/farabi-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nur-dev/farabi-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="nur-dev/farabi-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nur-dev/farabi-0.6B")
model = AutoModelForCausalLM.from_pretrained("nur-dev/farabi-0.6B")
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

vLLM

How to use nur-dev/farabi-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nur-dev/farabi-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nur-dev/farabi-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nur-dev/farabi-0.6B

SGLang

How to use nur-dev/farabi-0.6B 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 "nur-dev/farabi-0.6B" \
    --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": "nur-dev/farabi-0.6B",
		"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 "nur-dev/farabi-0.6B" \
        --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": "nur-dev/farabi-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nur-dev/farabi-0.6B with Docker Model Runner:
```
docker model run hf.co/nur-dev/farabi-0.6B
```

farabi-0.6B / README.md

nur-dev

Add interim evaluation results (BFCL v4 + multilingual MC)

30d2556 verified 7 days ago

preview code

raw

history blame contribute delete

8.78 kB

metadata

language:
  - kk
  - ru
  - en
pipeline_tag: text-generation
library_name: transformers
tags:
  - kazakh
  - multilingual
  - instruction-tuning
  - tool-calling
  - function-calling
  - agent
  - conversational
base_model: nur-dev/farabi-0.6B-base
license: apache-2.0

Farabi-0.6B

Farabi-0.6B is a compact, multilingual instruction-tuned language model with a primary focus on Kazakh, alongside strong Russian and English support. It is designed for everyday assistant use, reasoning, retrieval-grounded answering, and tool / function calling in agentic applications.

The model speaks fluent Kazakh and is intended to make high-quality conversational AI more accessible for the Kazakh language, where well-aligned models remain scarce.

Created by Nurgali Kadyrbek.

It is built on nur-dev/farabi-0.6B-base — a Kazakh-adapted base model that was itself continually pre-trained from Qwen3-0.6B — and then instruction-tuned to produce this assistant.

Highlights

🇰🇿 Kazakh-first — the majority of the instruction data is native Kazakh, with Russian and English mixed in for cross-lingual robustness.
🧠 Reasoning — supports optional step-by-step "thinking" mode that can be toggled on or off at request time.
🔧 Tool calling — emits Hermes-style <tool_call> blocks and is compatible with the OpenAI-style function-calling interface and agent frameworks.
📚 Grounded answering — trained to answer from provided documents and context, including longer inputs.
🪶 Small & deployable — 0.6B parameters, runs comfortably on a single modest GPU.

Languages

Language	Approx. share of instruction data
Kazakh (kk)	~56%
English (en)	~33%
Russian (ru)	~10%

Data coverage by domain

The model was instruction-tuned on a broad, internally curated mixture. Described in general terms (no technical specifics), the approximate domain composition is:

Domain	Approx. share
General instruction following & multi-turn conversation	~45%
Reasoning & step-by-step problem solving	~27%
Retrieval-grounded answering, long context & document Q&A	~13%
Tool use, function calling & agentic interaction	~7%
Knowledge, culture, news & encyclopedic content	~4%
Mathematics, language tasks (grammar / translation), safety & appropriate refusal, device & environment control, and assistant identity	~4%

Shares are approximate and reflect general domain proportions rather than exact figures.

Data provenance & acknowledgments

The training datasets were created internally by the author, including original synthesis as well as additionally processed and enriched material.

Approximately 5.4% of all data used for instruction tuning was derived (with additional processing and enrichment) from resources of two organizations, whose contributions to the Kazakh language are gratefully acknowledged:

Институт языкознания имени А. Байтурсынова — Institute of Linguistics named after A. Baitursynov
ННПЦ «Тіл-Қазына» имени Шайсултана Шаяхметова — Sh. Shayakhmetov National Research and Practical Center "Til-Qazyna"

Recommended sampling parameters

A good starting point for general use:

{
  "temperature": 0.15,
  "top_p": 0.95,
  "max_tokens": 1024,
  "repetition_penalty": 1.05,
  "stream": true,
  "chat_template_kwargs": {
    "enable_thinking": true
  },
  "continue_final_message": true
}

Set "enable_thinking": false to get direct answers without an explicit reasoning step. Raise temperature for more creative / open-ended generation.

Serving with vLLM

Start an OpenAI-compatible server with tool-calling enabled:

vllm serve nur-dev/farabi-0.6B \
  --served-model-name farabi-0.6b \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Query it with the standard OpenAI client (and the recommended sampling params):

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

resp = client.chat.completions.create(
    model="farabi-0.6b",
    messages=[
        {"role": "system", "content": "Сіз пайдалы әрі дәл көмекшісіз."},
        {"role": "user", "content": "Алматы туралы қысқаша айтып бер."},
    ],
    temperature=0.15,
    top_p=0.95,
    max_tokens=1024,
    extra_body={
        "repetition_penalty": 1.05,
        "chat_template_kwargs": {"enable_thinking": True},
    },
    stream=True,
)
for chunk in resp:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

Tool calling works through the standard tools=[...] argument — the model returns function calls that the server parses into structured tool_calls.

Serving with PyTorch / Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nur-dev/farabi-0.6B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "Сіз пайдалы әрі дәл көмекшісіз."},
    {"role": "user", "content": "Қазақстанның астанасы қай қала?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    enable_thinking=True,        # set False for direct answers
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.15,
    top_p=0.95,
    repetition_penalty=1.05,
)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Evaluation

⚠️ Interim results. The numbers below were measured on an early checkpoint (~17% through instruction tuning). They are expected to improve as training continues, but already show meaningful capability.

Tool / function calling — BFCL v4

Berkeley Function-Calling Leaderboard (v4), 1,040 cases, evaluated with the HuggingFace backend.

Category	Accuracy	n	What it measures
Simple	80.5%	322/400	one call, one tool available
Multiple	71.5%	143/200	pick the right tool from several
Parallel	65.5%	131/200	several calls in one turn
Irrelevance	5.4%	13/240	abstain when no tool fits
Overall	58.6%	609/1040
Function-calling avg	74.5%	596/800	excludes irrelevance

Takeaways:

Strong calling ability for a 0.6B model. When a call is warranted it is correct ~74.5% of the time — right tool, valid arguments, clean JSON — including 65.5% on the hard parallel / multi-call category.
The weakness is abstention, not calling. On queries that match no available tool, the model still tends to emit a call (irrelevance 5.4% → it over-triggers). This is the main driver of the lower overall score and the clearest area for improvement.

Multilingual comprehension — 4-way multiple choice

Multiple-choice comprehension across the model's three languages (random baseline = 25%), evaluated with the chat template and enable_thinking=False.

Language	Accuracy
English	53.7% ±1.7
Russian	50.0% ±1.7
Kazakh	41.8% ±1.6

Takeaways:

Well above the 25% random baseline in all three languages — real comprehension in English, Russian, and Kazakh.
Resource ordering (en > ru > kk) is as expected; Kazakh at 41.8% is clearly non-trivial.
Evaluating with the chat template and enable_thinking=False adds ~5–6 points per language versus a raw prompt — another reason to serve the model with its chat template (see serving instructions above).

Intended use & limitations

Farabi-0.6B is intended as a helpful general-purpose and agentic assistant, with a focus on Kazakh-language use cases. As a small model, it can make factual mistakes, and outputs should be verified for high-stakes or factual-critical applications. It should be used responsibly and in accordance with applicable laws and the base model's license.

Citation

If you use this model, please credit the author:

Nurgali Kadyrbek — Farabi-0.6B. https://www.linkedin.com/in/nurgali-kadyrbek-504260231/