Upload ShivikM4ForCausalLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
+## Bias, Risks, and Limitations
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+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
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+[More Information Needed]
+
+**APA:**
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+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
+
+## More Information [optional]
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+[More Information Needed]
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+## Model Card Authors [optional]
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+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "ShivikM4ForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_shivik_m4.ShivikM4Config",
+    "AutoModelForCausalLM": "modeling_shivik_m4.ShivikM4ForCausalLM"
+  },
+  "dtype": "bfloat16",
+  "head_dim": 64,
+  "hidden_size": 2048,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "shivik_m4",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 100000.0,
+  "transformers_version": "4.57.3",
+  "vocab_size": 49179
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.57.3"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30c64883c2a3cc74dcad686748325e36a75ff601668a3a54ede9c3a02abf63d2
+size 3422888544

modeling_shivik_m4.py

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+"""
+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