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Model: xxrickyxx/Ailo152m-events-en
Source: Original Platform

.gitattributes

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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+tags:
+- text-generation
+- information-extraction
+- structured-output
+- json
+- ner
+- small-language-model
+- edge-ai
+- on-device
+- ollama
+- gguf
+- calendar
+pipeline_tag: text-generation
+library_name: gguf
+model-index:
+- name: AILO-152M-Events-EN
+  results: []
+---
+
+# AILO-152M-Events-EN  Natural language → calendar-event JSON ⚡
+
+> **A 152M-parameter specialist** that turns an English sentence into a clean **event JSON** — title, date, time, location, participants — and runs on almost anything.
+
+This is a **task-specialist** built on AILO-152M. It does one thing and does it well: read an event description in plain English and output structured JSON. Tiny, fast, deterministic — ideal as the parsing brain of a calendar app, assistant, or automation.
+
+```bash
+ollama run Alieno/ailo-152m-events-en
+>>> Lunch with Sarah tomorrow at 1pm at the new Italian place
+{"title": "lunch", "date": "tomorrow", "time": "13:00", "location": "the new Italian place", "participants": ["Sarah"]}
+```
+
+## Schema
+
+```json
+{"title": str, "date": str|null, "time": "HH:MM"|null, "location": str|null, "participants": [str]}
+```
+
+- **time is normalized** to 24h `HH:MM` — *"at 3pm"* → `15:00`, *"half past 7"* → `07:30`, *"at noon"* → `12:00`.
+- **date is extracted as written** (*"tomorrow"*, *"next Friday"*, *"March 15"*) — it is **not** resolved to a calendar date (the model has no clock).
+- Missing fields → `null`; no participants → `[]`.
+
+## Benchmarks (held-out test set, 1500 unseen examples)
+
+| Metric | Score |
+|---|---|
+| **Valid JSON** | **100%** |
+| Full object exact-match | 83.7% |
+| `title` | 97.3% |
+| `date` | 88.3% |
+| `time` (normalized) | **100%** |
+| `location` | 97.0% |
+| `participants` | 97.3% |
+
+It also **generalizes to real, free-form sentences** (it learned to *copy spans*, not classify to a fixed list): *"Call mom tonight"* → `{"title": "call mom", ...}`, *"Birthday party Saturday at Jake's place with everyone"* → `{"title": "birthday party", "location": "Jake's place", "participants": ["everyone"]}`.
+
+## Use it in an app
+
+```bash
+curl http://localhost:11434/api/chat -d '{
+  "model": "Alieno/ailo-152m-events-en",
+  "messages": [{"role": "user", "content": "Quick sync with the dev team Monday 10am on Zoom"}],
+  "stream": false,
+  "options": {"temperature": 0.0}
+}'
+# -> {"title":"quick sync","date":"Monday","time":"10:00","location":"on Zoom","participants":["the dev team"]}
+```
+
+Tags: `:latest` / `:q8_0` (best, 156 MB) · `:q4_k_m` (smallest, 97 MB) · `:f16` (291 MB).
+Run with **temperature 0** for deterministic JSON. `repeat_penalty` is kept low (1.05) so JSON punctuation isn't penalized.
+
+## Details
+
+| Property | Value |
+|---|---|
+| Parameters | 151.9M |
+| Architecture | Decoder-only Transformer (LayerNorm · RoPE · SwiGLU), 12L/768/12H, ctx 512 |
+| Base | AILO-152M-v2 → specialized on event-extraction |
+| Training | 26k synthetic (sentence → JSON) pairs, open/compositional vocabulary (~2000 unique titles) so the model learns to **copy spans** |
+| Formats | GGUF (q4_k_m, q8_0, f16) + PyTorch |
+
+## Limitations
+
+- **Dates are not resolved** to absolute dates — the phrase is extracted as-is.
+- Unusual date phrasings (*"the 23rd of March"*) may drop the day number.
+- Single event per input; English only; 512-token context (short sentences).
+- For exact calendar entries, resolve the relative date downstream with the user's timezone/clock.
+
+## License & contact
+
+Dual-license: **CC BY-NC-SA 4.0** (free for research/education/personal) + **commercial** by separate agreement.
+**Riccardo Sparacino** — [LinkedIn](https://www.linkedin.com/in/riccardo-sparacino-developer-php-javascript-mysql-app-ios-android/)
+
+```bibtex
+@misc{ailo152m_events_en_2026,
+  title  = {AILO-152M-Events-EN: A tiny natural-language-to-event-JSON specialist},
+  author = {Sparacino, Riccardo}, year = {2026},
+  note   = {Dual-licensed CC BY-NC-SA 4.0 / commercial}
+}
+```

ailo-extract-en-f16.gguf

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ailo-extract-en-q4_k_m.gguf

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ailo-extract-en-q8_0.gguf

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config.json

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+{
+  "architectures": [
+    "AILOForCausalLM"
+  ],
+  "model_type": "ailo",
+  "vocab_size": 50257,
+  "hidden_size": 768,
+  "num_hidden_layers": 12,
+  "num_attention_heads": 12,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 512,
+  "hidden_dropout_prob": 0.1,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "pad_token_id": 50256,
+  "torch_dtype": "float32",
+  "transformers_version": "4.57.0",
+  "auto_map": {
+    "AutoConfig": "configuration_ailo.AILOConfig",
+    "AutoModelForCausalLM": "modeling_ailo.AILOForCausalLM"
+  }
+}

configuration_ailo.py

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+"""
+AILO Configuration for HuggingFace Transformers
+"""
+
+from transformers import PretrainedConfig
+
+
+class AILOConfig(PretrainedConfig):
+    """Configuration class for AILO model."""
+    
+    model_type = "ailo"
+    
+    def __init__(
+        self,
+        vocab_size: int = 50257,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        max_position_embeddings: int = 512,
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        bos_token_id: int = 50256,
+        eos_token_id: int = 50256,
+        pad_token_id: int = 50256,
+        **kwargs
+    ):
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            **kwargs
+        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob

modeling_ailo.py

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+"""
+AILO Model for HuggingFace Transformers - Matching original architecture
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from typing import Optional, Tuple, Union
+
+from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+try:
+    from .configuration_ailo import AILOConfig
+except ImportError:
+    from configuration_ailo import AILOConfig
+
+
+class RotaryPositionalEmbedding(nn.Module):
+    """Rotary Position Embedding (RoPE)."""
+    
+    def __init__(self, dim: int, max_seq_len: int = 512, base: int = 10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.max_seq_len = max_seq_len
+        
+    def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos(), emb.sin()
+
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    """Apply rotary position embedding."""
+    def rotate_half(x):
+        x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
+        return torch.cat((-x2, x1), dim=-1)
+    
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class AILOAttention(nn.Module):
+    """Multi-head attention matching original structure."""
+    
+    def __init__(self, config: AILOConfig):
+        super().__init__()
+        self.n_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.scale = self.head_dim ** -0.5
+        
+        # Match original: separate q, k, v projections
+        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.rotary = RotaryPositionalEmbedding(self.head_dim, config.max_position_embeddings)
+        
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, T, C = x.shape
+        
+        q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
+        
+        cos, sin = self.rotary(x, T)
+        cos, sin = cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+        
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        
+        # Causal mask
+        causal_mask = torch.triu(torch.ones(T, T, device=x.device), diagonal=1).bool()
+        attn = attn.masked_fill(causal_mask.unsqueeze(0).unsqueeze(0), float('-inf'))
+        
+        attn = F.softmax(attn, dim=-1)
+        attn = self.dropout(attn)
+        
+        out = (attn @ v).transpose(1, 2).reshape(B, T, C)
+        return self.out_proj(out)
+
+
+class AILOMLP(nn.Module):
+    """Feed-forward with SwiGLU - matching original w1, w2, w3 structure."""
+    
+    def __init__(self, config: AILOConfig):
+        super().__init__()
+        # Match original: w1 [3072, 768], w2 [768, 3072], w3 [3072, 768]
+        self.w1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.w2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.w3 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # SwiGLU: w2(silu(w1(x)) * w3(x))
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class AILOBlock(nn.Module):
+    """Transformer block matching original structure."""
+    
+    def __init__(self, config: AILOConfig):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.hidden_size, elementwise_affine=True, bias=False)
+        self.attn = AILOAttention(config)
+        self.ln2 = nn.LayerNorm(config.hidden_size, elementwise_affine=True, bias=False)
+        self.ff = AILOMLP(config)
+        
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(self.ln1(x))
+        x = x + self.ff(self.ln2(x))
+        return x
+
+
+class AILOPreTrainedModel(PreTrainedModel):
+    """Base class for AILO models."""
+    
+    config_class = AILOConfig
+    base_model_prefix = "ailo"
+    
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+
+class AILOForCausalLM(AILOPreTrainedModel, GenerationMixin):
+    """AILO model for causal language modeling - matching original structure."""
+    
+    def __init__(self, config: AILOConfig):
+        super().__init__(config)
+        
+        # Match original naming: tok_emb, blocks, ln_f, head
+        self.tok_emb = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.blocks = nn.ModuleList([AILOBlock(config) for _ in range(config.num_hidden_layers)])
+        self.ln_f = nn.LayerNorm(config.hidden_size, elementwise_affine=True, bias=False)
+        self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        # Weight tying
+        self.head.weight = self.tok_emb.weight
+        
+        self.post_init()
+    
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> CausalLMOutputWithPast:
+        x = self.tok_emb(input_ids)
+        
+        for block in self.blocks:
+            x = block(x)
+        
+        x = self.ln_f(x)
+        logits = self.head(x)
+        
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss = F.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+        
+        return CausalLMOutputWithPast(loss=loss, logits=logits)
+    
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids}
+    
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.LongTensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_k: int = 50,
+        top_p: float = 0.95,
+        **kwargs
+    ) -> torch.LongTensor:
+        """Generate text tokens."""
+        for _ in range(max_new_tokens):
+            idx_cond = input_ids[:, -512:]  # Max context
+            outputs = self(idx_cond)
+            logits = outputs.logits[:, -1, :] / temperature
+            
+            # Top-k
+            if top_k > 0:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = float('-inf')
+            
+            # Top-p
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                sorted_indices_to_remove[..., 0] = 0
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = float('-inf')
+            
+            probs = F.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1)
+            input_ids = torch.cat([input_ids, next_token], dim=1)
+            
+            if next_token.item() == self.config.eos_token_id:
+                break
+        
+        return input_ids

pytorch_model.bin

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