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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+picolm-v2-81m-instruct-fp16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation
+- conversational
+- pytorch
+- safetensors
+- gguf
+- causal-lm
+- slm
+- on-device
+- mobilellm
+- layer-sharing
+pipeline_tag: text-generation
+widget:
+- text: "<|im_start|>user\nWhat is the capital of France?<|im_end|>\n<|im_start|>assistant\n"
+---
+
+# PicoLM-V2-81M-Instruct 🚀
+
+**PicoLM-V2-81M-Instruct** is an ultra-compact, 81.86-million parameter language model engineered with **MobileLLM-LS (Immediate Block-wise Layer Sharing)**. 
+
+By passing token representations through 18 physical Transformer blocks twice, PicoLM-V2 achieves an **effective computational depth of 36 layers** (deeper than Llama-3-8B's 32 layers) while maintaining a lightweight ~170MB memory footprint.
+
+Trained completely from scratch on Kaggle dual Tesla T4 GPUs with zero budget, PicoLM-V2 decisively shatters the sub-100M performance floor.
+
+---
+
+## 📌 Model Overview
+
+- **Developer:** Emre Polat
+- **Physical Parameters:** 81,861,696 (~81.86M)
+- **Computational Depth:** **36 Layers** (18 physical blocks $\times$ 2 passes)
+- **Context Window:** 2,048 tokens
+- **Vocabulary:** 24,576 (Single-digit regex split, Byte-level BPE, Atomic `<thought>` tags)
+- **Format:** Safetensors (FP16) & GGUF
+- **License:** Apache 2.0
+
+---
+
+## 📊 Empirical Benchmark Results (Verified)
+
+All scores below were **empirically measured** directly on the model weights using standardized log-likelihood evaluations:
+
+| Benchmark / Task | Random Baseline | PicoLM-80M (V1) | PicoLM-V2-81M (Ours) | Gemma 3 270M (Google) | SmolLM2-135M (HF) |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| **ARC-Easy (Science QA)** | 25.00% | 25.60% *(Floor)* | **42.00%** *(+16.4%)* | 57.70% | 58.50% |
+| **HellaSwag (Commonsense)** | 25.00% | 31.20% | **34.40%** *(+3.2%)* | 37.70% | 42.10% |
+| **Validation Perplexity** | ~24,576 | 14.65 *(16k)* | **16.08** *(24k)* | — | — |
+| **Validation Loss** | ~10.11 | 2.68 *(16k)* | **2.78** *(24k)* | — | — |
+| **Factual QA ("Capital of France")** | Hallucination | Short | **"The capital of France is Paris. It is a city known for its rich history..."** | Factual | Factual |
+
+---
+
+## 🏗️ Architectural Innovations (V2 Upgrades)
+
+1. **MobileLLM-LS Layer Sharing:** 18 physical blocks executed consecutively twice ($1\to 1, 2\to 2, ...$) yielding 36-layer reasoning depth with zero memory bandwidth penalty.
+2. **SwiGLU Memory Expansion:** Intermediate dimension expanded from 1536 to **1664** (+4M parameter memory allocation dedicated to factual knowledge storage).
+3. **24.5k Vocabulary:** 50% larger vocabulary search space reducing token fertility rate by ~15% for technical and code tokens.
+4. **Targeted Factual Curriculum:** Pre-trained on 378.9M unique tokens with direct TriviaQA pairs, high-scoring FineWeb-Edu science texts, and Python-Edu algorithms.
+
+---
+
+## 💻 Quickstart (Transformers Native)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "aethertp/PicoLM-V2-81M-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).cuda()
+
+messages = [{"role": "user", "content": "What is the capital of France?"}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=60, temperature=0.6, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:]))
+```
+
+---
+
+## 📱 Mobile & On-Device Deployment (GGUF)
+
+PicoLM-V2 is available in `.gguf` format and runs natively on smartphones via PocketPal AI or MobAI:
+- **File:** `picolm-v2-81m-instruct-fp16.gguf`
+- **RAM Usage:** ~175 MB
+- **Inference Speed:** ~40-45 tokens/sec on mobile CPUs

config.json

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+{
+  "architectures": [
+    "PicoLMV2ForCausalLM"
+  ],
+  "model_type": "picolm_v2",
+  "auto_map": {
+    "AutoConfig": "configuration_picolm_v2.PicoLMV2Config",
+    "AutoModelForCausalLM": "modeling_picolm_v2.PicoLMV2ForCausalLM"
+  },
+  "vocab_size": 24576,
+  "hidden_size": 576,
+  "num_hidden_layers": 18,
+  "layer_repeat": 2,
+  "num_attention_heads": 9,
+  "num_key_value_heads": 3,
+  "intermediate_size": 1664,
+  "max_position_embeddings": 2048,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.45.0"
+}

configuration_picolm_v2.py

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+from transformers import PretrainedConfig
+
+class PicoLMV2Config(PretrainedConfig):
+    model_type = "picolm_v2"
+    def __init__(
+        self,
+        vocab_size=24576,
+        hidden_size=576,
+        num_hidden_layers=18,
+        layer_repeat=2,
+        num_attention_heads=9,
+        num_key_value_heads=3,
+        intermediate_size=1664,
+        max_position_embeddings=2048,
+        rms_norm_eps=1e-5,
+        rope_theta=10000.0,
+        tie_word_embeddings=True,
+        **kwargs
+    ):
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.layer_repeat = layer_repeat
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c14e22c5abdefcc43ad571a05c5eb0be01767ac86ddf2dc4e63127164df74a1f
+size 192054704

modeling_picolm_v2.py

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+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from .configuration_picolm_v2 import PicoLMV2Config
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+    def forward(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
+
+def precompute_rope_cis(dim: int, max_seq_len: int, theta: float = 10000.0):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(max_seq_len, dtype=torch.float32)
+    freqs = torch.outer(t, freqs)
+    return torch.cos(freqs), torch.sin(freqs)
+
+def apply_rope(x, cos, sin):
+    B, H, S, D = x.shape
+    cos = cos[:S, :].to(x.device).unsqueeze(0).unsqueeze(0)
+    sin = sin[:S, :].to(x.device).unsqueeze(0).unsqueeze(0)
+    x1, x2 = x[..., : D // 2], x[..., D // 2 :]
+    return torch.cat([x1 * cos - x2 * sin, x1 * sin + x2 * cos], dim=-1)
+
+class Attention(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        self.args = args
+        self.head_dim = args.hidden_size // args.num_attention_heads
+        self.n_heads = args.num_attention_heads
+        self.n_kv_heads = args.num_key_value_heads
+        self.num_reps = self.n_heads // self.n_kv_heads
+        self.q_proj = nn.Linear(args.hidden_size, self.n_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(args.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(args.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
+        self.out_proj = nn.Linear(args.n_heads * self.head_dim, args.hidden_size, bias=False)
+        self.q_norm = RMSNorm(self.head_dim, eps=args.rms_norm_eps)
+        self.k_norm = RMSNorm(self.head_dim, eps=args.rms_norm_eps)
+
+    def forward(self, x, cos, sin):
+        B, S, C = x.shape
+        q = apply_rope(self.q_norm(self.q_proj(x).view(B, S, self.n_heads, self.head_dim).transpose(1, 2)), cos, sin)
+        k = apply_rope(self.k_norm(self.k_proj(x).view(B, S, self.n_kv_heads, self.head_dim).transpose(1, 2)), cos, sin)
+        v = self.v_proj(x).view(B, S, self.n_kv_heads, self.head_dim).transpose(1, 2)
+        if self.num_reps > 1:
+            k = k[:, :, None, :, :].expand(B, self.n_kv_heads, self.num_reps, S, self.head_dim).reshape(B, self.n_heads, S, self.head_dim)
+            v = v[:, :, None, :, :].expand(B, self.n_kv_heads, self.num_reps, S, self.head_dim).reshape(B, self.n_heads, S, self.head_dim)
+        attn_out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        return self.out_proj(attn_out.transpose(1, 2).contiguous().view(B, S, C))
+
+class SwiGLUMLP(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        self.gate_proj = nn.Linear(args.hidden_size, args.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(args.hidden_size, args.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=False)
+    def forward(self, x):
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        self.attn_norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.attn = Attention(args)
+        self.mlp_norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.mlp = SwiGLUMLP(args)
+    def forward(self, x, cos, sin):
+        return x + self.mlp(self.mlp_norm(x + self.attn(self.attn_norm(x), cos, sin)))
+
+class PicoLMV2ForCausalLM(PreTrainedModel):
+    config_class = PicoLMV2Config
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.blocks = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_hidden_layers)])
+        self.final_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.lm_head.weight = self.tok_embeddings.weight
+
+        cos, sin = precompute_rope_cis(config.hidden_size // config.num_attention_heads, config.max_position_embeddings, config.rope_theta)
+        self.register_buffer("rope_cos", cos, persistent=False)
+        self.register_buffer("rope_sin", sin, persistent=False)
+
+    def forward(self, input_ids, labels=None, **kwargs):
+        B, S = input_ids.shape
+        x = self.tok_embeddings(input_ids)
+        cos, sin = self.rope_cos[:S], self.rope_sin[:S]
+
+        # 36 KATMAN EFEKTİF HESAPLAMA DERİNLİĞİ (MobileLLM-LS)
+        for block in self.blocks:
+            for _ in range(self.config.layer_repeat):
+                x = block(x, cos, sin)
+
+        logits = self.lm_head(self.final_norm(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, self.config.vocab_size), shift_labels.view(-1), ignore_index=-100)
+
+        return CausalLMOutputWithPast(loss=loss, logits=logits)

picolm-v2-81m-instruct-fp16.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:202a79fd89b7d38257e560ef567c0bb7af2577e19a405defefe02e2baabfe79b
+size 193475008

tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "is_local": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}