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config.json

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+    "model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.norm.weight": "model-00002-of-00002.safetensors"
+  }
+}

modeling_shivik_m4.py

@@ -0,0 +1,331 @@
+"""
+SHIVIK-M4 Model Architecture (SmolLM2-Compatible)
+==================================================
+Matched to SmolLM2-1.7B for weight loading:
+- 24 layers, 2048 hidden, 32 heads (MHA - all heads are KV heads)
+- Full RoPE, SwiGLU MLP, RMSNorm
+"""
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class ShivikM4Config(PretrainedConfig):
+    model_type = "shivik_m4"
+
+    def __init__(
+        self,
+        vocab_size=49152,
+        hidden_size=2048,
+        intermediate_size=8192,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        num_key_value_heads=32,  # MHA for SmolLM2 compatibility
+        head_dim=64,
+        rms_norm_eps=1e-5,
+        max_position_embeddings=4096,
+        rope_theta=100000.0,
+        tie_word_embeddings=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.rms_norm_eps = rms_norm_eps
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class ShivikM4RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        dtype = x.dtype
+        x = x.float()
+        norm = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(norm + self.eps)
+        return (self.weight * x).to(dtype)
+
+
+class ShivikM4RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings, base=10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.max_seq_len_cached = max_position_embeddings
+        self._set_cos_sin_cache(max_position_embeddings)
+
+    def _set_cos_sin_cache(self, seq_len):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat([freqs, freqs], dim=-1)
+        self.register_buffer("cos_cached", emb.cos().unsqueeze(0).unsqueeze(0), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().unsqueeze(0).unsqueeze(0), persistent=False)
+
+    def forward(self, x, seq_len):
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len)
+        return (
+            self.cos_cached[:, :, :seq_len, :].to(x.dtype),
+            self.sin_cached[:, :, :seq_len, :].to(x.dtype),
+        )
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    cos = cos.squeeze(0).squeeze(0)
+    sin = sin.squeeze(0).squeeze(0)
+    cos = cos[position_ids].unsqueeze(1)
+    sin = sin[position_ids].unsqueeze(1)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class ShivikM4Attention(nn.Module):
+    def __init__(self, config: ShivikM4Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.num_kv_heads = config.num_key_value_heads
+        self.num_kv_groups = self.num_heads // self.num_kv_heads
+        self.scale = 1.0 / math.sqrt(self.head_dim)
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = ShivikM4RotaryEmbedding(
+            self.head_dim, config.max_position_embeddings, config.rope_theta
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_ids=None,
+        past_key_value=None,
+        use_cache=False,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+
+        q = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(hidden_states).view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(hidden_states).view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
+
+        past_kv_len = 0
+        if past_key_value is not None and past_key_value[0] is not None:
+            past_kv_len = past_key_value[0].shape[2]
+
+        cos, sin = self.rotary_emb(v, seq_len=past_kv_len + q_len)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
+
+        if past_key_value is not None and past_key_value[0] is not None:
+            k = torch.cat([past_key_value[0], k], dim=2)
+            v = torch.cat([past_key_value[1], v], dim=2)
+
+        present_kv = (k, v) if use_cache else None
+
+        # GQA expansion (for MHA, num_kv_groups=1, so this is a no-op)
+        if self.num_kv_groups > 1:
+            k_expanded = k.repeat_interleave(self.num_kv_groups, dim=1)
+            v_expanded = v.repeat_interleave(self.num_kv_groups, dim=1)
+        else:
+            k_expanded = k
+            v_expanded = v
+
+        attn_weights = torch.matmul(q, k_expanded.transpose(2, 3)) * self.scale
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
+        attn_output = torch.matmul(attn_weights, v_expanded)
+
+        attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, self.hidden_size)
+        return self.o_proj(attn_output), present_kv
+
+
+class ShivikM4MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+    def forward(self, x):
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class ShivikM4DecoderLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.input_layernorm = ShivikM4RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.self_attn = ShivikM4Attention(config)
+        self.post_attention_layernorm = ShivikM4RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.mlp = ShivikM4MLP(config)
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, use_cache=False):
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, present_kv = self.self_attn(
+            hidden_states, attention_mask, position_ids, past_key_value, use_cache
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states, present_kv
+
+
+class ShivikM4Model(PreTrainedModel):
+    config_class = ShivikM4Config
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([ShivikM4DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = ShivikM4RMSNorm(config.hidden_size, config.rms_norm_eps)
+
+    def _make_causal_mask(self, q_len, kv_len, dtype, device):
+        if q_len == kv_len:
+            mask = torch.full((q_len, kv_len), torch.finfo(dtype).min, dtype=dtype, device=device)
+            mask = torch.triu(mask, diagonal=1)
+        else:
+            mask = torch.zeros((q_len, kv_len), dtype=dtype, device=device)
+        return mask[None, None, :, :]
+
+    def forward(self, input_ids, attention_mask=None, position_ids=None, past_key_values=None, use_cache=None):
+        bsz, seq_len = input_ids.shape
+
+        past_len = 0
+        if past_key_values is not None and past_key_values[0] is not None and past_key_values[0][0] is not None:
+            past_len = past_key_values[0][0].shape[2]
+
+        if position_ids is None:
+            position_ids = torch.arange(past_len, past_len + seq_len, device=input_ids.device).unsqueeze(0)
+
+        hidden_states = self.embed_tokens(input_ids)
+
+        kv_len = past_len + seq_len
+        causal_mask = self._make_causal_mask(seq_len, kv_len, hidden_states.dtype, hidden_states.device)
+
+        if attention_mask is not None:
+            padding_mask = (1.0 - attention_mask[:, None, None, :].to(hidden_states.dtype)) * torch.finfo(hidden_states.dtype).min
+            causal_mask = causal_mask + padding_mask
+
+        next_cache = () if use_cache else None
+        for i, layer in enumerate(self.layers):
+            past_kv = past_key_values[i] if past_key_values is not None else None
+            hidden_states, present_kv = layer(hidden_states, causal_mask, position_ids, past_kv, use_cache)
+            if use_cache:
+                next_cache += (present_kv,)
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states, next_cache
+
+
+class ShivikM4ForCausalLM(PreTrainedModel, GenerationMixin):
+    config_class = ShivikM4Config
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ShivikM4Model(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        use_cache=None,
+        labels=None,
+        **kwargs,
+    ):
+        outputs = self.model(input_ids, attention_mask, position_ids, past_key_values, use_cache)
+        hidden_states, past_key_values = outputs
+
+        logits = self.lm_head(hidden_states)
+
+        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),
+            )
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=past_key_values,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **kwargs):
+        past_len = 0
+        if past_key_values is not None and past_key_values[0] is not None and past_key_values[0][0] is not None:
+            past_len = past_key_values[0][0].shape[2]
+            input_ids = input_ids[:, -1:]
+
+        position_ids = torch.arange(
+            past_len, past_len + input_ids.shape[1],
+            dtype=torch.long, device=input_ids.device
+        ).unsqueeze(0)
+
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache", True),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+        }
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered = ()
+        for layer_past in past_key_values:
+            reordered += (
+                tuple(state.index_select(0, beam_idx) for state in layer_past),
+            )
+        return reordered

special_tokens_map.json

@@ -0,0 +1,42 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,168 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<repo_name>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<reponame>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<file_sep>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<filename>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<gh_stars>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<issue_closed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<jupyter_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<jupyter_text>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<jupyter_code>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<jupyter_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<jupyter_script>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<empty_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 8192,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}