Instructions to use nur-dev/farabi-1.7b-agent-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nur-dev/farabi-1.7b-agent-rag with Transformers:

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

pipe = pipeline("text-generation", model="nur-dev/farabi-1.7b-agent-rag")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nur-dev/farabi-1.7b-agent-rag")
model = AutoModelForCausalLM.from_pretrained("nur-dev/farabi-1.7b-agent-rag")
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]:]))

HERMES

How to use nur-dev/farabi-1.7b-agent-rag with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nur-dev/farabi-1.7b-agent-rag with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nur-dev/farabi-1.7b-agent-rag"
# 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-1.7b-agent-rag",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nur-dev/farabi-1.7b-agent-rag

SGLang

How to use nur-dev/farabi-1.7b-agent-rag 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-1.7b-agent-rag" \
    --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-1.7b-agent-rag",
		"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-1.7b-agent-rag" \
        --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-1.7b-agent-rag",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nur-dev/farabi-1.7b-agent-rag with Docker Model Runner:
```
docker model run hf.co/nur-dev/farabi-1.7b-agent-rag
```

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Farabi-1.7B-agent-rag

A 1.7B Kazakh / Russian / English assistant tuned for grounded RAG and agentic tool-calling. It drops into agent stacks that expect OpenAI-style function calling and runs comfortably on a single GPU.

Built on Qwen3-1.7B and adapted for Kazakh, Russian, and English.

Capabilities

Grounded RAG. Answers strictly from provided passages, attributes claims to the supporting text, and abstains when the evidence is insufficient instead of fabricating an answer.
Tool-calling (Hermes / OpenAI function calling). Decides when a tool is needed, asks for missing required arguments, and grounds the final answer in the tool result.
- Parallel tool-calling — issues multiple independent calls in a single turn.
- Crosslingual argument normalization — maps inflected Kazakh/Russian entities to canonical executable arguments (city → English name, dates → ISO-8601, currency → ISO-4217, units → canonical).
- Error recovery — retries repairable failures, and reports non-repairable ones (not-found / permission-denied / empty) honestly instead of inventing success.
Prompt-injection resistance. Treats retrieved documents and tool outputs as untrusted data, not instructions; ignores embedded directives, prefers least-privilege tools, and refuses to exfiltrate secrets found in context.
Text workbench. Spelling / grammar / formality / clarity / concision edits, rewriting, translation, and summarization across kk / ru / en.
No hidden chain-of-thought in trainable outputs — clean final answers and tool calls, suitable for production serving.

Benchmarks

Agentic & RAG capabilities (held-out probe)

Capability	Score
Prompt-injection resistance (overall)	96%
• instruction-in-retrieved-chunk	100%
• tool-output injection	100%
• least-privilege tool use	100%
• secret / data-exfiltration refusal	82%
Parallel tool-calling	94%
Crosslingual argument normalization	91%
Text editing / workbench	86%

Note: secret-exfiltration refusal (82%) is the model's weakest safety dimension — for credential-bearing contexts, pair the model with an output filter.

Academic (ISSAI Kazakh/Russian QOLDA suite, n=250/bench; RAGBench = chrF)

Accuracy (%), compared with same-size and larger models for context. AVG is the mean of the 10 accuracy benchmarks.

Model	Size	ARC-kk	ARC-ru	MMLU-kk	GPQA-kk	GPQA-ru	GSM8k-kk	GSM8k-ru	PolyMath-kk	MMLU-Pro-kk	MMLU-Pro-ru	RAGBench (chrF)	AVG
Farabi-1.7B-agent-rag	1.7B	58.8	74.4	35.2	28.8	24.0	32.4	50.4	14.0	14.8	22.4	25.4	35.5
ISSAI foggen-1.7B	1.7B	45.6	77.6	31.6	31.2	22.8	35.2	68.4	20.0	11.6	24.0	33.5	36.8
Qwen3-1.7B	1.7B	47.6	78.4	31.6	26.4	14.4	40.4	72.8	14.4	12.8	14.4	36.0	35.3
ISSAI Sherkala-8B-Chat	8B	74.8	78.4	47.6	30.0	25.6	68.8	80.0	20.4	20.4	22.4	41.0	46.8

Farabi-1.7B is competitive with the same-size ISSAI foggen-1.7B and Qwen3-1.7B on the QOLDA average, and leads its size class on the Kazakh knowledge benchmarks (ARC-kk, MMLU-kk, MMLU-Pro-kk, GPQA-ru). Sherkala-8B is shown as a larger-model reference point.

Translation (FLORES-200, BLEU)

Direction	BLEU
ru → en	24.4
en → ru	18.5
kk → en	17.3
kk → ru	8.3
en → kk	8.2
ru → kk	7.7

Serving

Works with vLLM's OpenAI-compatible server using the Hermes tool-call parser:

vllm serve nur-dev/farabi-1.7b-agent-rag \
  --chat-template chat_template.jinja \
  --enable-auto-tool-choice --tool-call-parser hermes

Then call it with the OpenAI SDK (and the OpenAI Agents SDK):

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")

resp = client.chat.completions.create(
    model="nur-dev/farabi-1.7b-agent-rag",
    messages=[{"role": "user", "content": "Бүгін Алматыда ауа райы қандай?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Current weather for a city.",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string", "description": "Canonical English city name."}},
                "required": ["city"],
            },
        },
    }],
    tool_choice="auto",
)
print(resp.choices[0].message.tool_calls)

Languages

Kazakh (kk), Russian (ru), English (en).

License

CC BY-NC 4.0 — non-commercial use only. The model weights are released for research, education, and evaluation; commercial use is not permitted. Built on Qwen3-1.7B (Apache-2.0); the base-model components remain under their original Apache-2.0 terms.

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Safetensors

Model size

2B params

Tensor type

BF16

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