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)

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.

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)

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.