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README.md

@@ -1,54 +1,24 @@
 ---
-license: apache-2.0
+language: en
 tags:
-- tiny
-- lumia
-- custom-pytorch
-- alibi
-- gelu
+  - tiny
+  - reasoning
+  - alibi
+  - gqa
 ---
 
-# Lumia-Tiny
+# Lumia Tiny (V2)
 
-1,017,088 parameter causal language model with ALiBi (Attention with Linear Biases) and GELU activation.
+Custom PyTorch reasoning SFT model. ~1.1M params.
 
 ## Architecture
-
-| Config | Value |
-|--------|-------|
-| Parameters | 1,017,088 |
-| Hidden size | 128 |
-| Intermediate | 512 |
-| Layers | 2 |
-| Heads | 8 (query), 4 (key/value) |
-| Max seq len | 2048 |
-| Vocab | 4096 (BPE, byte fallback) |
-| Position | ALiBi (no learned embeddings) |
-| Activation | GELU |
-| Tie embeddings | Yes |
+- ALiBi + GQA (8 heads, 4 KV heads)
+- RMSNorm + SiLU gating
+- Tied embeddings, head_dim=16
+- Vocab: 1757 (no filler)
 
 ## Usage
-
 ```python
-import torch
-from model_tiny import TinyModel
-
-model = TinyModel.from_pretrained("samcheng0/lumia-tiny")
-tokenizer = ...
-
-inputs = tokenizer("Hello world", return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=50)
-print(tokenizer.decode(outputs[0]))
+from scripts.model_tiny import TinyModel
+model = TinyModel()  # V2 defaults
 ```
-
-### GGUF (llama.cpp)
-
-The model is also available in GGUF Q4_K_M format:
-
-```
-tiny.gguf
-```
-
-## Training
-
-Trained on [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) via SFT.

model_tiny.py

@@ -62,6 +62,17 @@ class ALiBiAttention(nn.Module):
         return self.o_proj(out)
 
 
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.eps = eps
+
+    def forward(self, x):
+        norm = x.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
+        return (x.float() * norm).type_as(x) * self.weight
+
+
 class MLP(nn.Module):
     def __init__(self, hidden: int, intermediate: int):
         super().__init__()
@@ -70,15 +81,15 @@ class MLP(nn.Module):
         self.down = nn.Linear(intermediate, hidden, bias=False)
 
     def forward(self, x):
-        return self.down(F.gelu(self.gate(x)) * self.up(x))
+        return self.down(F.silu(self.gate(x)) * self.up(x))
 
 
 class TransformerBlock(nn.Module):
     def __init__(self, hidden: int, intermediate: int, num_heads: int, num_kv_heads: int):
         super().__init__()
-        self.ln1 = nn.LayerNorm(hidden)
+        self.ln1 = RMSNorm(hidden)
         self.attn = ALiBiAttention(hidden, num_heads, num_kv_heads)
-        self.ln2 = nn.LayerNorm(hidden)
+        self.ln2 = RMSNorm(hidden)
         self.mlp = MLP(hidden, intermediate)
 
     def forward(self, x):
@@ -88,8 +99,8 @@ class TransformerBlock(nn.Module):
 
 
 class TinyModel(nn.Module):
-    def __init__(self, vocab_size=4096, hidden=128, intermediate=512,
-                 num_layers=2, num_heads=8, num_kv_heads=4, max_seq_len=2048,
+    def __init__(self, vocab_size=1757, hidden=128, intermediate=640,
+                 num_layers=3, num_heads=8, num_kv_heads=4, max_seq_len=2048,
                  tie_weights=True):
         super().__init__()
         self.hidden = hidden
@@ -99,7 +110,7 @@ class TinyModel(nn.Module):
             TransformerBlock(hidden, intermediate, num_heads, num_kv_heads)
             for _ in range(num_layers)
         ])
-        self.ln_f = nn.LayerNorm(hidden)
+        self.ln_f = RMSNorm(hidden)
         self.lm_head = nn.Linear(hidden, vocab_size, bias=False)
         if tie_weights:
             self.lm_head.weight = self.token_embed.weight
@@ -200,8 +211,8 @@ def count_params(model):
 
 def create_model():
     model = TinyModel(
-        vocab_size=4096, hidden=128, intermediate=512,
-        num_layers=2, num_heads=8, num_kv_heads=4,
+        vocab_size=1757, hidden=128, intermediate=640,
+        num_layers=3, num_heads=8, num_kv_heads=4,
         max_seq_len=2048, tie_weights=True,
     )
     n = count_params(model)

tokenizer.json

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train_tiny.yaml

@@ -1,8 +1,8 @@
-# Tiny Training Config — Custom PyTorch (ALiBi+GELU, 1M params)
+# Tiny Training Config — Custom PyTorch (ALiBi+SiLU+RMSNorm, V2)
 # Script: scripts/model_tiny.py
 
 model:
-  name_or_path: "tiny-custom-pytorch"
+  name_or_path: "tiny-custom-pytorch-v2"
 
 data:
   # Local JSONL (used when hf_repo is not set)
@@ -19,7 +19,7 @@ data:
 
 training:
   output_dir: "outputs/tiny-sft"
-  run_name: "lumia-tiny-v1"
+  run_name: "lumia-tiny-v2"
   per_device_train_batch_size: 2
   per_device_eval_batch_size: 2
   gradient_accumulation_steps: 4