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Model: TuwaiqAcademy/AISA-AR-FunctionCall-Think Source: Original Platform
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README.md
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README.md
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---
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license: gemma
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language:
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- ar
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base_model:
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- google/gemma-3-270m
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- function-calling
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- tool-use
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- agentic
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- arabic
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- reasoning
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- think
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- gemma3
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- shared-task
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- arabicnlp2026
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- baseline
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- dialect
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datasets:
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- TuwaiqAcademy/AISA-ArabicFC
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model-index:
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- name: AISA-AR-FunctionCall-Think
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results:
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- task:
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type: text-generation
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name: Arabic Function Calling — Track B (Reasoning-Augmented)
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dataset:
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name: AISA-ArabicFC (held-out test)
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type: TuwaiqAcademy/AISA-ArabicFC
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metrics:
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- type: function-name-accuracy
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value: 0.982
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name: FnAcc
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- type: argument-exact-match
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value: 0.541
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name: ArgEM
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- type: think-before-call-rate
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value: 0.868
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name: ThinkRate
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- type: overall
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value: 0.739
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name: Overall (Track B, v2)
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---
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# AISA-AR-FunctionCall-Think
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### 🏷️ Official **Track B baseline** for the [AISA-ArabicFC shared task](https://huggingface.co/spaces/Omartificial-Intelligence-Space/AISA-ArabicFC-Shared-Task) @ **ArabicNLP 2026** (co-located with EMNLP 2026, Budapest)
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> This model is the **organizer-provided baseline** for **Track B — Reasoning-Augmented Function Calling**. It defines the reference score that participating systems are expected to beat. It is released for reproducibility and as a starting point — **it is not a competition entry.**
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A compact (**270M-parameter**) Arabic function-calling model that, given an Arabic user query (in any of 5 dialects) and a set of candidate tools, **writes a short Arabic `<think>` reasoning trace and then emits a structured tool call**. Fine-tuned (LoRA) from **[google/gemma-3-270m](https://huggingface.co/google/gemma-3-270m)** on the AISA-ArabicFC reasoning data.
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For the non-reasoning Track A baseline, see the sibling model **[AISA-AR-FunctionCall-FT](https://huggingface.co/AISA-Framework/AISA-AR-FunctionCall-FT)**.
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---
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## At a glance
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| | |
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|---|---|
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| **Role** | Official baseline — Track B (Reasoning-Augmented) |
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| **Base model** | google/gemma-3-270m (270M params) |
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| **Adaptation** | LoRA fine-tune (merged), then full causal-LM inference |
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| **Languages** | Arabic — MSA, Gulf, Egyptian, Levantine, Maghrebi |
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| **Behaviour** | `<think>` Arabic reasoning → structured function call |
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| **Training data** | [TuwaiqAcademy/AISA-ArabicFC](https://huggingface.co/datasets/TuwaiqAcademy/AISA-ArabicFC)
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| **License** | Gemma (see *License* below) |
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---
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## The shared task
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Given an Arabic user query and a set of candidate tool definitions, a system must:
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1. **Decide** whether a function call is required (some queries need no tool),
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2. **Select** the correct function name,
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3. **Extract** the structured arguments,
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4. **(Track B)** **Generate an Arabic reasoning trace** (`<think> … </think>`) *before* the call.
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| Track | Description |
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|-------|-------------|
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| **A — Core** | Decide / Select / Extract |
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| **B — Reasoning-Augmented** ← *this model* | Track A **+** an Arabic `<think>` reasoning trace |
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| **C — Cross-Dialect Robustness** | Diagnostic: dialect-stratified evaluation of A/B submissions |
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---
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## How it works — input / output format
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This model uses **Gemma 3 chat turns** with a custom function-calling schema (it does **not** emit plain JSON). The exact prompt is the `text` field in the dataset; the structure is:
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```
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<bos><start_of_turn>developer
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<system instruction in Arabic>
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<start_function_declaration>declaration:NAME{description:<escape>…<escape>,parameters:{…}}<end_function_declaration>
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…one declaration per candidate tool…<end_of_turn>
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<start_of_turn>developer
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التاريخ والوقت الحالي …: 2024-04-12T23:05:24
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اليوم هو الجمعة
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أنت نموذج يمكنه استدعاء الوظائف التالية<end_of_turn>
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<start_of_turn>user
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أريد مقارنة أسعار تلفاز سامسونج في الأردن<end_of_turn>
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<start_of_turn>model
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```
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The model then generates:
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```
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<think>
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يبدو أن نية المستخدم هي الحصول على مقارنة لأسعار تلفاز سامسونج في الأردن. أداة "compare_prices" هي الأنسب …
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</think>
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<start_function_call>call:compare_prices{country:<escape>Jordan<escape>,product_name:<escape>Samsung TV<escape>}<end_function_call>
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```
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For a query that needs **no tool**, the model omits the `<start_function_call>` block (→ `requires_function = false`).
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---
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## Usage
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```python
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import re, torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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MODEL_ID = "TuwaiqAcademy/AISA-AR-FunctionCall-Think"
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tok = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, torch_dtype=torch.float32, device_map="auto"
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).eval()
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def parse_model_output(text: str) -> dict:
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"""Turn raw generation into the shared-task submission schema."""
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out = {"requires_function": False, "function_name": "none", "arguments": {}, "think": ""}
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if (m := re.search(r"<think>\s*(.*?)\s*</think>", text, re.DOTALL)):
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out["think"] = m.group(1).strip()
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if (m := re.search(r"<start_function_call>\s*call:(\w+)\{(.*?)\}\s*<end_function_call>", text, re.DOTALL)):
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out["requires_function"] = True
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out["function_name"] = m.group(1)
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for key, str_val, num_val in re.findall(r"(\w+):(?:<escape>(.*?)<escape>|([^,}]+))", m.group(2)):
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val = str_val if str_val else num_val
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try:
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val = float(val) if "." in str(val) else int(val)
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except (ValueError, TypeError):
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pass
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out["arguments"][key] = val
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return out
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# Easiest path: take the ready-made prompt from the dataset's `text` field and
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# cut it at the model turn (everything after is what the model should produce).
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from datasets import load_dataset
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row = load_dataset("TuwaiqAcademy/AISA-ArabicFC", split="validation")[0]
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prompt = row["text"].split("<start_of_turn>model\n")[0] + "<start_of_turn>model\n"
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inputs = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
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with torch.no_grad():
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gen = model.generate(**inputs, max_new_tokens=250, do_sample=False) # greedy
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raw = tok.decode(gen[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
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print(parse_model_output(raw))
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# → {'requires_function': True, 'function_name': 'compare_prices',
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# 'arguments': {'country': 'Jordan', 'product_name': 'Samsung TV'},
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# 'think': 'يبدو أن نية المستخدم …'}
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```
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The parsed dict maps directly onto a **leaderboard submission line**: `{"id", "tool_called", "arguments", "think"}` (use `function_name` → `tool_called`).
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---
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## Evaluation
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Scored on the AISA-ArabicFC **held-out test set** (1,000 positive + negative examples) using the official **v2** metrics:
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- **FnAcc** — function-name accuracy over *all* samples (also penalises hallucinated / missed calls; negatives have gold `none`)
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- **ArgEM** — strict argument **exact match**, over positives only
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- **ThinkRate** — fraction of outputs with a non-empty `<think>` trace
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- **Overall (Track A)** = `0.40·FnAcc + 0.60·ArgEM`
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- **Overall (Track B)** = `0.30·FnAcc + 0.50·ArgEM + 0.20·ThinkRate`
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### Baseline results
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| System | FnAcc | ArgEM | Overall (A) | Overall (B) |
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|--------|:-----:|:-----:|:-----------:|:-----------:|
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| **AISA-AR-FunctionCall-Think (270M) ← this** | **0.982** | **0.541** | **0.717** | **0.739** |
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| GPT-4o — zero-shot | 0.927 | 0.070 | 0.413 | 0.313 |
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| GPT-4o — 3-shot | 0.854 | 0.122 | 0.415 | 0.317 |
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| Random baseline | 0.047 | 0.033 | 0.039 | 0.031 |
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- **Think-Before-Call rate (ThinkRate):** **0.868** for this model; 0.000 for all non-reasoning baselines.
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- **Hallucination rate:** **0.000** on negative (no-tool) queries.
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**Key takeaways**
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- 🎯 **Argument extraction is the open challenge.** Tool *selection* is largely solved (FnAcc ≈ 0.98), but strict argument **exact match tops out at 0.541** — and GPT-4o reaches only 0.070 zero-shot. This is where the task is won or lost.
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- 🪶 **A 270M model beats GPT-4o** across every metric here, showing the value of task-specific Arabic training and lowering the compute barrier to entry.
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- 🗣️ **Cross-dialect gaps remain.** FnAcc varies by roughly 10–15 points across dialects, with **Gulf and Levantine** consistently the hardest and Maghrebi (small sample) the easiest — see the Track C diagnostic in the task overview paper.
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---
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## Training
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- **Base:** `google/gemma-3-270m`
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- **Method:** LoRA (rank 64), 3 epochs, cosine LR scheduler
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- **Data:** AISA-ArabicFC training split (~10.5K examples) with 12,000 Arabic reasoning annotations for the `<think>` traces
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- **Objective:** produce a short Arabic reasoning trace followed by a single structured tool call (or no call for negatives)
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---
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## Intended use & limitations
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**Intended use**
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- A reference **baseline** to compare against and reproduce for the AISA-ArabicFC shared task.
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- A lightweight starting point for Arabic tool-use / agentic experiments.
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**Out of scope / limitations**
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- Trained for the **27-tool, 8-domain AISA-ArabicFC schema** and its prompt format; behaviour on arbitrary tools or free-form chat is undefined.
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- Single-turn, single-call setting — no multi-tool or multi-turn dialogue.
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- **Argument extraction is imperfect** (ArgEM 0.541): expect errors in date normalisation, numeric typing, and dialectal argument phrasing.
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- Uneven dialect coverage (Maghrebi is only ~1.3% of data); robustness varies by dialect.
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- A 270M model — capacity-limited by design to keep the baseline accessible.
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---
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## Related resources
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- 🏆 **Shared task page:** https://huggingface.co/spaces/Omartificial-Intelligence-Space/AISA-ArabicFC-Shared-Task
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- 📊 **Leaderboard:** https://huggingface.co/spaces/TuwaiqAcademy/AISA-ArabicFC-SharedTask-Leaderboard
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- 📚 **Dataset (train + dev):** [TuwaiqAcademy/AISA-ArabicFC](https://huggingface.co/datasets/TuwaiqAcademy/AISA-ArabicFC)
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---
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## Citation
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```bibtex
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@inproceedings{najar2026aisaarabicfc,
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title = {AISA-ArabicFC: Arabic Function Calling for Agentic AI Systems},
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author = {Najar, Omar},
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booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Conference (ArabicNLP 2026)},
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year = {2026}
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}
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```
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## License
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This model is a derivative of **Gemma 3** and is distributed under the **[Gemma Terms of Use](https://ai.google.dev/gemma/terms)**. By using it you agree to those terms and to the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). The AISA-ArabicFC **dataset** is released separately under Apache-2.0.
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## Contact
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Shared-task organizers — **trdc@tuwaiq.edu.sa** · Tuwaiq Academy
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```
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added_tokens.json
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{
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"<end_of_image>": 262145,
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"<image_soft_token>": 262144
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}
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chat_template.jinja
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{%- macro format_parameters(properties, required) -%}
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{%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
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{%- set ns = namespace(found_first=false) -%}
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{%- for key, value in properties | dictsort -%}
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{%- if key not in standard_keys -%}
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{%- if ns.found_first %},{% endif -%}
|
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{%- set ns.found_first = true -%}
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{{- key }}:{description:<escape>{{ value['description'] }}<escape>
|
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{%- if value['type'] | upper == 'STRING' -%}
|
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{%- if value['enum'] -%}
|
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,enum:{{ format_argument(value['enum']) }}
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{%- endif -%}
|
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{%- elif value['type'] | upper == 'OBJECT' -%}
|
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,properties:{
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{%- if value['properties'] is defined and value['properties'] is mapping -%}
|
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{{- format_parameters(value['properties'], value['required'] | default([])) -}}
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{%- elif value is mapping -%}
|
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{{- format_parameters(value, value['required'] | default([])) -}}
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{%- endif -%}
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}
|
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{%- if value['required'] -%}
|
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,required:[
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{%- for item in value['required'] | default([]) -%}
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<escape>{{- item -}}<escape>
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{%- if not loop.last %},{% endif -%}
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{%- endfor -%}
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]
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{%- endif -%}
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{%- elif value['type'] | upper == 'ARRAY' -%}
|
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{%- if value['items'] is mapping and value['items'] -%}
|
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,items:{
|
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{%- set ns_items = namespace(found_first=false) -%}
|
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{%- for item_key, item_value in value['items'] | dictsort -%}
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{%- if item_value is not none -%}
|
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{%- if ns_items.found_first %},{% endif -%}
|
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{%- set ns_items.found_first = true -%}
|
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{%- if item_key == 'properties' -%}
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properties:{
|
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{%- if item_value is mapping -%}
|
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{{- format_parameters(item_value, value['items']['required'] | default([])) -}}
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{%- endif -%}
|
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}
|
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{%- elif item_key == 'required' -%}
|
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required:[
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{%- for req_item in item_value -%}
|
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<escape>{{- req_item -}}<escape>
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{%- if not loop.last %},{% endif -%}
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{%- endfor -%}
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]
|
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{%- elif item_key == 'type' -%}
|
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{%- if item_value is string -%}
|
||||
type:{{ format_argument(item_value | upper) }}
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{%- else -%}
|
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type:{{ format_argument(item_value | map('upper') | list) }}
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||||
{%- endif -%}
|
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{%- else -%}
|
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{{ item_key }}:{{ format_argument(item_value) }}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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||||
}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
,type:<escape>{{ value['type'] | upper }}<escape>}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
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{%- endmacro -%}
|
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{% macro format_function_declaration(tool_data) -%}
|
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declaration:{{- tool_data['function']['name'] -}}
|
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{description:<escape>{{- tool_data['function']['description'] -}}<escape>
|
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{%- set params = tool_data['function']['parameters'] -%}
|
||||
{%- if params -%}
|
||||
,parameters:{
|
||||
{%- if params['properties'] -%}
|
||||
properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
|
||||
{%- endif -%}
|
||||
{%- if params['required'] -%}
|
||||
required:[
|
||||
{%- for item in params['required'] -%}
|
||||
<escape>{{- item -}}<escape>
|
||||
{{- ',' if not loop.last -}}
|
||||
{%- endfor -%}
|
||||
],
|
||||
{%- endif -%}
|
||||
{%- if params['type'] -%}
|
||||
type:<escape>{{- params['type'] | upper -}}<escape>}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
}
|
||||
{%- endmacro -%}
|
||||
{% macro format_argument(argument, escape_keys=True) -%}
|
||||
{%- if argument is string -%}
|
||||
{{- '<escape>' + argument + '<escape>' -}}
|
||||
{%- elif argument is boolean -%}
|
||||
{%- if argument -%}
|
||||
{{- 'true' -}}
|
||||
{%- else -%}
|
||||
{{- 'false' -}}
|
||||
{%- endif -%}
|
||||
{%- elif argument is mapping -%}
|
||||
{{- '{' -}}
|
||||
{%- set ns = namespace(found_first=false) -%}
|
||||
{%- for key, value in argument | dictsort -%}
|
||||
{%- if ns.found_first %},{% endif -%}
|
||||
{%- set ns.found_first = true -%}
|
||||
{%- if escape_keys -%}
|
||||
{{- '<escape>' + key + '<escape>' -}}
|
||||
{%- else -%}
|
||||
{{- key -}}
|
||||
{%- endif -%}
|
||||
:{{- format_argument(value, escape_keys=escape_keys) -}}
|
||||
{%- endfor -%}
|
||||
{{- '}' -}}
|
||||
{%- elif argument is sequence -%}
|
||||
{{- '[' -}}
|
||||
{%- for item in argument -%}
|
||||
{{- format_argument(item, escape_keys=escape_keys) -}}
|
||||
{%- if not loop.last %},{% endif -%}
|
||||
{%- endfor -%}
|
||||
{{- ']' -}}
|
||||
{%- else -%}
|
||||
{{- argument -}}
|
||||
{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
{{ bos_token }}
|
||||
{%- set ns = namespace(prev_message_type=None) -%}
|
||||
{#- Tool Declarations -#}
|
||||
{%- set loop_messages = messages -%}
|
||||
{%- if tools or messages[0]['role'] == 'system' or messages[0]['role'] == 'developer' -%}
|
||||
{{- '<start_of_turn>developer\n' -}}
|
||||
{%- if messages[0]['role'] == 'system' or messages[0]['role'] == 'developer' -%}
|
||||
{%- if messages[0]['content'] is string -%}
|
||||
{{- messages[0]['content'] | trim -}}
|
||||
{%- elif messages[0]['content'] is sequence -%}
|
||||
{%- for item in messages[0]['content'] -%}
|
||||
{%- if item['type'] == 'text' -%}
|
||||
{{- item['text'] | trim -}}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- endif -%}
|
||||
{%- set loop_messages = messages[1:] -%}
|
||||
{%- else -%}
|
||||
{{- 'You are a model that can do function calling with the following functions' -}}
|
||||
{%- set loop_messages = messages -%}
|
||||
{%- endif -%}
|
||||
{%- if tools -%}
|
||||
{%- for tool in tools %}
|
||||
{{- '<start_function_declaration>' -}}
|
||||
{{- format_function_declaration(tool) | trim }}
|
||||
{{- '<end_function_declaration>' -}}
|
||||
{%- endfor %}
|
||||
{%- endif -%}
|
||||
{{- '<end_of_turn>\n' }}
|
||||
{%- endif %}
|
||||
{#- Loop through messages. -#}
|
||||
{%- for message in loop_messages -%}
|
||||
{%- if (message['role'] == 'assistant') -%}
|
||||
{#- Rename "assistant" to "model". -#}
|
||||
{%- set role = "model" -%}
|
||||
{%- else -%}
|
||||
{%- set role = message['role'] -%}
|
||||
{%- endif -%}
|
||||
{%- if role != 'tool' -%}
|
||||
{%- if ns.prev_message_type != 'tool_response' -%}
|
||||
{{- '<start_of_turn>' + role + '\n' }}
|
||||
{%- endif -%}
|
||||
{%- set ns.prev_message_type = None -%}
|
||||
{%- if 'content' in message and message['content'] is not none -%}
|
||||
{%- if message['content'] is string -%}
|
||||
{{ message['content'] | trim }}
|
||||
{%- elif message['content'] is sequence -%}
|
||||
{%- for item in message['content'] -%}
|
||||
{%- if item['type'] == 'image' -%}
|
||||
{{ '<start_of_image>' }}
|
||||
{%- elif item['type'] == 'text' -%}
|
||||
{{ item['text'] | trim }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid content type in user/assistant message") }}
|
||||
{%- endif -%}
|
||||
{%- set ns.prev_message_type = 'content' -%}
|
||||
{%- endif -%}
|
||||
{%- if 'tool_calls' in message and message['tool_calls'] and message['tool_calls'] is iterable -%}
|
||||
{#- Tool Calls -#}
|
||||
{%- for tool_call in message['tool_calls'] -%}
|
||||
{% set function = tool_call['function'] %}
|
||||
{{- '<start_function_call>call:' + function['name'] + '{' -}}
|
||||
{%- if 'arguments' in function -%}
|
||||
{%- if function['arguments'] is mapping -%}
|
||||
{%- set ns = namespace(found_first=false) -%}
|
||||
{%- for key, value in function['arguments'] | dictsort -%}
|
||||
{%- if ns.found_first %},{% endif -%}
|
||||
{%- set ns.found_first = true -%}
|
||||
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
||||
{%- endfor -%}
|
||||
{%- elif function['arguments'] is string -%}
|
||||
{# This handles string-JSON, just in case #}
|
||||
{{ function['arguments'] }}
|
||||
{%- endif %}
|
||||
{%- endif -%}
|
||||
{{- '}<end_function_call>' -}}
|
||||
{%- endfor -%}
|
||||
{%- if loop.last -%}
|
||||
{{ '<start_function_response>' }}
|
||||
{%- endif -%}
|
||||
{%- set ns.prev_message_type = 'tool_call' -%}
|
||||
{%- endif -%}
|
||||
{%- else -%}
|
||||
{#- Tool Responses -#}
|
||||
{%- if 'content' in message and message['content'] -%}
|
||||
{%- if message['content'] is mapping -%}
|
||||
{%- if 'name' in message['content'] and 'response' in message['content'] -%}
|
||||
{{ '<start_function_response>response:' + message['content']['name'] | trim + '{' }}
|
||||
{%- set response_ns = namespace(found_first=false) -%}
|
||||
{%- for key, value in message['content']['response'] | dictsort -%}
|
||||
{%- if response_ns.found_first %},{% endif -%}
|
||||
{%- set response_ns.found_first = true -%}
|
||||
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
||||
{%- endfor -%}
|
||||
{{- '}<end_function_response>' -}}
|
||||
{%- elif 'name' in message -%}
|
||||
{{ '<start_function_response>response:' + message['name'] | trim + '{' }}
|
||||
{%- set response_ns = namespace(found_first=false) -%}
|
||||
{%- for key, value in message['content'] | dictsort -%}
|
||||
{%- if response_ns.found_first %},{% endif -%}
|
||||
{%- set response_ns.found_first = true -%}
|
||||
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
||||
{%- endfor -%}
|
||||
{{- '}<end_function_response>' -}}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid tool response mapping: must contain 'name' and 'response' keys, or 'name' must be in the message.") }}
|
||||
{%- endif -%}
|
||||
{%- elif message['content'] is string -%}
|
||||
{%- if 'name' in message -%}
|
||||
{{ '<start_function_response>response:' + message['name'] | trim + '{value:' + format_argument(message['content'], escape_keys=False) + '}<end_function_response>' }}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid tool response: 'name' must be provided.") }}
|
||||
{%- endif -%}
|
||||
{%- elif message['content'] is sequence -%}
|
||||
{%- for item in message['content'] -%}
|
||||
{%- if item is mapping -%}
|
||||
{%- if 'name' in item and 'response' in item -%}
|
||||
{{ '<start_function_response>response:' + item['name'] | trim + '{' }}
|
||||
{%- set response_ns = namespace(found_first=false) -%}
|
||||
{%- for key, value in item['response'] | dictsort -%}
|
||||
{%- if response_ns.found_first %},{% endif -%}
|
||||
{%- set response_ns.found_first = true -%}
|
||||
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
||||
{%- endfor -%}
|
||||
{{- '}<end_function_response>' -}}
|
||||
{%- elif 'name' in message -%}
|
||||
{{ '<start_function_response>response:' + message['name'] | trim + '{' }}
|
||||
{%- set response_ns = namespace(found_first=false) -%}
|
||||
{%- for key, value in item | dictsort -%}
|
||||
{%- if response_ns.found_first %},{% endif -%}
|
||||
{%- set response_ns.found_first = true -%}
|
||||
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
||||
{%- endfor -%}
|
||||
{{- '}<end_function_response>' -}}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid tool response mapping: must contain 'name' and 'response' keys, or 'name' must be in the message.") }}
|
||||
{%- endif -%}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid tool response message: multiple responses must all be mappings") }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid content type in tool message: must be mapping, sequence of mappings, or string.") }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- set ns.prev_message_type = 'tool_response' -%}
|
||||
{%- endif -%}
|
||||
{%- if ns.prev_message_type not in ['tool_call', 'tool_response'] -%}
|
||||
{{ '<end_of_turn>\n' }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- if add_generation_prompt -%}
|
||||
{%- if ns.prev_message_type != 'tool_response' -%}
|
||||
{{- '<start_of_turn>model\n' -}}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
56
config.json
Normal file
56
config.json
Normal file
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"_sliding_window_pattern": 6,
|
||||
"architectures": [
|
||||
"Gemma3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"attn_logit_softcapping": null,
|
||||
"bos_token_id": 2,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 106,
|
||||
"final_logit_softcapping": null,
|
||||
"head_dim": 256,
|
||||
"hidden_activation": "gelu_pytorch_tanh",
|
||||
"hidden_size": 640,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 2048,
|
||||
"layer_types": [
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"model_type": "gemma3_text",
|
||||
"num_attention_heads": 4,
|
||||
"num_hidden_layers": 18,
|
||||
"num_key_value_heads": 1,
|
||||
"pad_token_id": 0,
|
||||
"query_pre_attn_scalar": 256,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_local_base_freq": 10000.0,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 512,
|
||||
"transformers_version": "4.57.4",
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.2.1",
|
||||
"use_bidirectional_attention": false,
|
||||
"use_cache": true,
|
||||
"vocab_size": 262144
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 2,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
1,
|
||||
50,
|
||||
106
|
||||
],
|
||||
"max_length": 32768,
|
||||
"pad_token_id": 0,
|
||||
"top_k": 64,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.57.4"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:744d3bba6c65015853039ff50f984b1a0581df38f4615c71a91ef57d0b7a0011
|
||||
size 536334056
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"boi_token": "<start_of_image>",
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eoi_token": "<end_of_image>",
|
||||
"eos_token": {
|
||||
"content": "<end_of_turn>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"image_token": "<image_soft_token>",
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sfr_token": "<start_function_response>",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b6b09a0b4a803ad453063ca4bb49a784540e8120004e2450e025df2b27d41fb2
|
||||
size 33384899
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:aa009fcbc3589a9904d30d04834094fea4653c2ac6d2de2cd1262d4f7a50ceb3
|
||||
size 4689144
|
||||
51355
tokenizer_config.json
Normal file
51355
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user