182 lines
7.7 KiB
Markdown
182 lines
7.7 KiB
Markdown
---
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license: apache-2.0
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language:
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- ar
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base_model:
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- oddadmix/50M-2048-Emhotob
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- arabic
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- tool-calling
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- function-calling
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- grpo
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- rlhf
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- distillation
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- small-language-model
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- slm
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- tiny
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- llama
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- from-scratch
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- proof-of-concept
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---
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# Emhotob-50M-GRPO — Arabic Tool-Calling (RL-tuned)
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A **~51.8M-parameter** Arabic **tool-calling / function-calling** model, tuned with
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**GRPO** (Group Relative Policy Optimization) reinforcement learning on top of a
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distilled SFT checkpoint. It is a proof-of-concept for **agentic Arabic behavior at
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tiny scale** — deciding *when to call a tool* vs. *when to decline*, entirely from a
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model small enough to run on a CPU.
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This checkpoint corresponds to the **GRPO-v12** run in the training repo: it is tuned
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for **robust abstention** — it declines out-of-scope requests reliably instead of
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forcing a tool call.
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- **Base model:** [`oddadmix/50M-2048-Emhotob`](https://huggingface.co/oddadmix/50M-2048-Emhotob)
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— Llama-architecture, pre-trained **from scratch on ~20B Arabic tokens**, 2048 ctx.
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- **Architecture & recipe:** derived from [`SupraLabs/Supra-50M-Base`](https://huggingface.co/SupraLabs/Supra-50M-Base);
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a **POC for training Arabic tiny models from scratch**.
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- **Format:** ChatML + Hermes-style `<tool_call>{...}</tool_call>`.
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> **الملخص بالعربية:** نموذج عربي صغير (~51.8 مليون معامل) لاستدعاء الأدوات (Tool/Function
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> Calling)، مبني على معمارية Llama ومدرَّب من الصفر، ثم ضُبط باستخدام التعلّم المعزّز
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> **GRPO**. هذه النسخة مُحسَّنة للامتناع عن استدعاء أداة عند عدم توفّر أداة مناسبة، بدلًا من
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> اختلاق استدعاء خاطئ. نموذج تجريبي (POC) لسلوك عربي «وكيلي» على نطاق صغير جدًا.
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---
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## Model details
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| | |
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|---|---|
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| **Parameters** | ~51.8M |
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| **Architecture** | Llama (`LlamaForCausalLM`) |
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| **Hidden size** | 512 · **Layers** 12 · **Heads** 8 (GQA, 4 KV) · **head_dim** 64 |
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| **Vocab size** | 32002 (32000 base + 2 ChatML control tokens) |
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| **Context length** | 2048 |
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| **Chat format** | ChatML (`<|im_start|>` / `<|im_end|>`) + Hermes `<tool_call>` |
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| **Precision** | bfloat16 |
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| **License** | Apache-2.0 (matches the base architecture) |
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---
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## How it was trained
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The model is the end of a **base → SFT → distillation → RL** pipeline, all in pure
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🤗 Transformers (**no TRL** — the GRPO loop is from scratch):
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1. **Base:** [`oddadmix/50M-2048-Emhotob`](https://huggingface.co/oddadmix/50M-2048-Emhotob),
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pre-trained from scratch on ~20B Arabic tokens (2048 ctx), architecture and training
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scripts derived from [`SupraLabs/Supra-50M-Base`](https://huggingface.co/SupraLabs/Supra-50M-Base).
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2. **SFT:** supervised fine-tuning on Arabic tool-calling data (ChatML + Hermes
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`<tool_call>` format).
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3. **Sequence-level distillation (Kim & Rush, 2016):** a 1.2B tool-specialized teacher
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([`LiquidAI/LFM2-1.2B-Tool`](https://huggingface.co/LiquidAI/LFM2-1.2B-Tool))
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generated targets — including **fluent Arabic abstentions** and hard-negative
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(no-matching-tool) examples — that the student learned to imitate. This is the
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variant that reliably **declines out-of-scope requests**.
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4. **GRPO (Shao et al., 2024 — DeepSeekMath):** on-policy RL with a **verifiable reward**
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(the tool-eval scorer itself — no reward model, no LLM judge). For each prompt the
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policy samples a group of completions; a group-relative advantage plus a KL leash to
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the reference model shapes the call/abstain decision. This checkpoint uses a
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**balanced 1:1 call/abstain** batch with a gentle learning rate, which **holds the
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high-abstention behavior** while keeping JSON/tool-selection intact.
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**What the experiment found (the POC result):** at 50M parameters there is a genuine
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**precision ↔ recall frontier** on the call decision — you can maximize valid calls *or*
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maximize correct refusals, but not both. GRPO with a perfect verifiable reward moves
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*along* this frontier rather than beyond it; this checkpoint deliberately sits at the
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**robust-abstention** corner. (A separately-tested 2.5× larger student tied these
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metrics, corroborating that the ceiling is capacity, not the training signal.)
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---
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## Evaluation
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Numbers are reported honestly for this exact checkpoint against the sibling SFT/RL
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checkpoints (see the training repo for the full trial log).
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**Dialect eval (18 items, Egyptian-dialect prompts):**
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| metric | this model | note |
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|---|---|---|
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| strict success | 12/18 | headline |
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| decision (call/answer correct) | 16/18 | |
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| **abstention** | **4/4** ✅ | robustly declines out-of-scope requests |
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**MSA eval v2 (126 items, higher-resolution):** strict 11/68, decision 49/68,
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**abstain 27/46** — the high-abstention end of the decision↔abstain frontier.
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Tool-selection (~33%) and argument-exactness (~18%) are the capacity-bound bottlenecks
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shared across all checkpoints at this scale.
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---
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## Usage
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The tokenizer uses the `TokenizersBackend` class, which requires **`transformers>=5.12`**.
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Build the ChatML prompt manually and pass the tool schema in the system message:
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```python
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import json, torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "oddadmix/Emhotob-50M-GPRO-Arabic-Final"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
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tools = [{
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"name": "get_weather",
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"description": "Get the current weather for a city",
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"parameters": {"type": "object",
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"properties": {"location": {"type": "string"}},
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"required": ["location"]},
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}]
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system = (
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"أنت مساعد يستطيع استدعاء الأدوات. الأدوات المتاحة:\n"
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+ json.dumps(tools, ensure_ascii=False)
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+ "\nإذا لم تكن هناك أداة مناسبة، اعتذر ولا تختلق استدعاءً."
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)
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user = "ما حالة الطقس في القاهرة؟"
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prompt = (
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f"<|im_start|>system\n{system}<|im_end|>\n"
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f"<|im_start|>user\n{user}<|im_end|>\n"
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f"<|im_start|>assistant\n"
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)
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ids = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(
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**ids, max_new_tokens=256, do_sample=False,
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repetition_penalty=1.2,
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eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"),
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)
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print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True))
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# Expected: <tool_call>{"name": "get_weather", "arguments": {"location": "القاهرة"}}</tool_call>
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```
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For an out-of-scope request with no matching tool, the model is tuned to **refuse in
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Arabic** rather than hallucinate a call.
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---
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## Intended use & limitations
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**Intended use.** Research and demonstration of Arabic tool-calling / agentic behavior at
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tiny scale; a CPU-friendly baseline for from-scratch Arabic SLM experiments.
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**Limitations.** At ~50M parameters this is a **proof of concept**. Tool-selection with
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several confusable tools (~33% correct) and exact argument extraction (~18%) are the
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capacity-bound weak spots — validate any tool call before executing it. Training was
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predominantly Modern Standard Arabic; dialect prompts are harder. Not for production
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agents without a validation/guardrail layer.
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---
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## Citation & credits
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- **Method:** GRPO — Shao et al., 2024 (*DeepSeekMath*); sequence-level KD — Kim & Rush, 2016.
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- **Teacher:** [`LiquidAI/LFM2-1.2B-Tool`](https://huggingface.co/LiquidAI/LFM2-1.2B-Tool).
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- **Base architecture & training scripts:** [`SupraLabs/Supra-50M-Base`](https://huggingface.co/SupraLabs/Supra-50M-Base) (Apache-2.0).
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- **Base Arabic model:** [`oddadmix/50M-2048-Emhotob`](https://huggingface.co/oddadmix/50M-2048-Emhotob) — from-scratch, ~20B Arabic tokens, 2048 ctx.
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