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LMConfig.py

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+from transformers import PretrainedConfig
+from typing import List
+
+
+class LMConfig(PretrainedConfig):
+    model_type = "minimind"
+
+    def __init__(
+            self,
+            dim: int = 768,
+            n_layers: int = 16,
+            n_heads: int = 16,
+            n_kv_heads: int = 8,
+            vocab_size: int = 6400,
+            hidden_dim: int = None,
+            multiple_of: int = 64,
+            norm_eps: float = 1e-5,
+            max_seq_len: int = 512,
+            dropout: float = 0.0,
+            flash_attn: bool = True,
+            ####################################################
+            # Here are the specific configurations of MOE
+            # When use_moe is false, the following is invalid
+            ####################################################
+            use_moe: bool = False,
+            num_experts_per_tok=2,
+            n_routed_experts=4,
+            n_shared_experts: bool = True,
+            scoring_func='softmax',
+            aux_loss_alpha=0.01,
+            seq_aux=True,
+            norm_topk_prob=True,
+            **kwargs,
+    ):
+        self.dim = dim
+        self.n_layers = n_layers
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.vocab_size = vocab_size
+        self.hidden_dim = hidden_dim
+        self.multiple_of = multiple_of
+        self.norm_eps = norm_eps
+        self.max_seq_len = max_seq_len
+        self.dropout = dropout
+        self.flash_attn = flash_attn
+        ####################################################
+        # Here are the specific configurations of MOE
+        # When use_moe is false, the following is invalid
+        ####################################################
+        self.use_moe = use_moe
+        self.num_experts_per_tok = num_experts_per_tok  # 每个token选择的专家数量
+        self.n_routed_experts = n_routed_experts  # 总的专家数量
+        self.n_shared_experts = n_shared_experts  # 共享专家
+        self.scoring_func = scoring_func  # 评分函数，默认为'softmax'
+        self.aux_loss_alpha = aux_loss_alpha  # 辅助损失的alpha参数
+        self.seq_aux = seq_aux  # 是否在序列级别上计算辅助损失
+        self.norm_topk_prob = norm_topk_prob  # 是否标准化top-k概率
+        super().__init__(**kwargs)

config.json

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+{
+  "architectures": [
+    "Transformer"
+  ],
+  "auto_map": {
+    "AutoConfig": "LMConfig.LMConfig",
+    "AutoModelForCausalLM": "model.Transformer"
+  },
+  "aux_loss_alpha": 0.01,
+  "dim": 768,
+  "dropout": 0.0,
+  "flash_attn": true,
+  "hidden_dim": null,
+  "max_seq_len": 512,
+  "model_type": "minimind",
+  "multiple_of": 64,
+  "n_heads": 16,
+  "n_kv_heads": 8,
+  "n_layers": 16,
+  "n_routed_experts": 4,
+  "n_shared_experts": true,
+  "norm_eps": 1e-05,
+  "norm_topk_prob": true,
+  "num_experts_per_tok": 2,
+  "scoring_func": "softmax",
+  "seq_aux": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "use_moe": false,
+  "vocab_size": 6400
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.37.2"
+}

model.py

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+import math
+import struct
+import inspect
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+class Attention(nn.Module):
+    def __init__(self, args: LMConfig):
+        super().__init__()
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        assert args.n_heads % self.n_kv_heads == 0
+        model_parallel_size = 1
+        self.n_local_heads = args.n_heads // model_parallel_size
+        self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = args.dim // args.n_heads
+        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+        self.attn_dropout = nn.Dropout(args.dropout)
+        self.resid_dropout = nn.Dropout(args.dropout)
+        self.dropout = args.dropout
+
+        # use flash attention or a manual implementation?
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
+
+        if not self.flash:
+            # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+            mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+            mask = torch.triu(mask, diagonal=1)
+            self.register_buffer("mask", mask)
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            pos_cis: torch.Tensor,
+            use_kv_cache: bool = False,
+            past_kv: Tuple[torch.Tensor] = None
+    ):
+        bsz, seqlen, _ = x.shape
+        # QKV
+        # inference
+        if use_kv_cache:
+            # 只计算最后一个token的Q
+            current_token = x[:, -1:, :]
+
+            if not past_kv:
+                xq = self.wq(x)
+                xk, xv = self.wk(x), self.wv(x)
+            else:
+                past_key, past_value = past_kv
+                xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(current_token)), dim=1)
+                xk = torch.cat((past_key, self.wk(current_token)), dim=1)
+                xv = torch.cat((past_value, self.wv(current_token)), dim=1)
+
+            past_kv = (xk, xv)
+        else:
+            xq = self.wq(x)
+            xk, xv = self.wk(x), self.wv(x)
+
+        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+
+        # RoPE relative positional embeddings
+        xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+
+        # grouped multiquery attention: expand out keys and values
+        xk = repeat_kv(xk, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
+        xv = repeat_kv(xv, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
+
+        # make heads into a batch dimension
+        xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
+        xk = xk.transpose(1, 2)
+        xv = xv.transpose(1, 2)
+
+        # flash implementation
+        if self.flash:
+            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None,
+                                                                      dropout_p=self.dropout if self.training else 0.0,
+                                                                      is_causal=True)
+        else:
+            # manual implementation
+            scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
+            assert hasattr(self, 'mask')
+            scores = scores + self.mask[:, :, :seqlen, :seqlen]  # (bs, n_local_heads, seqlen, cache_len + seqlen)
+            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+            scores = self.attn_dropout(scores)
+            output = torch.matmul(scores, xv)  # (bs, n_local_heads, seqlen, head_dim)
+
+        # restore time as batch dimension and concat heads
+        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+
+        # final projection into the residual stream
+        output = self.wo(output)
+        output = self.resid_dropout(output)
+        return output, past_kv
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
+        super().__init__()
+        if hidden_dim is None:
+            hidden_dim = 4 * dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        # topk selection algorithm
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        ### compute gating score
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        ### select top-k experts
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        ### norm gate to sum 1
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        ### expert-level computation auxiliary loss
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            # always compute aux loss based on the naive greedy topk method
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = None
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(
+                dim=config.dim,
+                hidden_dim=config.hidden_dim,
+                multiple_of=config.multiple_of,
+                dropout=config.dropout,
+            )
+            for _ in range(config.n_routed_experts)
+        ])
+
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(
+                dim=config.dim,
+                hidden_dim=config.hidden_dim,
+                multiple_of=config.multiple_of,
+                dropout=config.dropout,
+            )
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+
+        if self.training:
+            # 训练模式下，重复输入数据
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i])
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            # 推理模式下，只选择最优专家
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 例如当tokens_per_expert=[6, 15, 20, 26, 33, 38, 46, 52]
+        # 当token_idxs=[3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...]
+        # 意味着当token_idxs[:6] -> [3,  7, 19, 21, 24, 25,  4]位置的token都由专家0处理，token_idxs[6:15]位置的token都由专家1处理......
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            # 使用 scatter_add_ 进行 sum 操作
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, layer_id: int, args: LMConfig):
+        super().__init__()
+        self.n_heads = args.n_heads
+        self.dim = args.dim
+        self.head_dim = args.dim // args.n_heads
+        self.attention = Attention(args)
+
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
+
+        if args.use_moe:
+            self.feed_forward = MOEFeedForward(args)
+        else:
+            self.feed_forward = FeedForward(
+                dim=args.dim,
+                hidden_dim=args.hidden_dim,
+                multiple_of=args.multiple_of,
+                dropout=args.dropout,
+            )
+
+    def forward(self, x, pos_cis, use_kv_cache=False, past_kv: Tuple[torch.Tensor] = None):
+        attn_res, past_kv = self.attention(self.attention_norm(x), pos_cis, use_kv_cache, past_kv)
+        h = x + attn_res
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out, past_kv
+
+
+class Transformer(PreTrainedModel):
+    config_class = LMConfig
+    last_loss: Optional[torch.Tensor]
+
+    def __init__(self, params: LMConfig = None):
+        super().__init__(params)
+        if not params:
+            params = LMConfig()
+        self.params = params
+        self.vocab_size = params.vocab_size
+        self.n_layers = params.n_layers
+
+        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
+        self.dropout = nn.Dropout(params.dropout)
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(self.n_layers):
+            self.layers.append(TransformerBlock(layer_id, params))
+        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
+        self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
+
+        # share the unembedding parameters with the embedding parameters
+        self.tok_embeddings.weight = self.output.weight  # https://paperswithcode.com/method/weight-tying
+
+        # some useful precompute for the RoPE relative positional embeddings
+        pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
+        self.register_buffer("pos_cis", pos_cis, persistent=False)
+
+        # init all weights
+        self.apply(self._init_weights)
+        # apply special scaled init to the residual projections, per GPT-2 paper
+        for pn, p in self.named_parameters():
+            if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
+                torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers))
+
+        # Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
+        self.last_loss = None
+        self.OUT = CausalLMOutputWithPast()
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, tokens: Optional[torch.Tensor] = None,
+                targets: Optional[torch.Tensor] = None,
+                use_kv_cache=False, past_kvs=None, **keyargs):
+        if past_kvs is None:
+            past_kvs = [None for _ in range(self.n_layers)]
+        if 'input_ids' in keyargs:
+            tokens = keyargs['input_ids']
+        if 'attention_mask' in keyargs:
+            targets = keyargs['attention_mask']
+
+        _bsz, seqlen = tokens.shape
+        h = self.tok_embeddings(tokens)
+        h = self.dropout(h)
+        pos_cis = self.pos_cis[:seqlen]
+        for idx, layer in enumerate(self.layers):
+            h, past_kvs[idx] = layer(h, pos_cis, use_kv_cache, past_kvs[idx])
+
+        h = self.norm(h)
+
+        if targets is not None:
+            # if we are given some desired targets also calculate the loss
+            logits = self.output(h)
+            self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+        else:
+            # inference-time mini-optimization: only forward the output on the very last position
+            logits = self.output(h[:, [-1], :])  # note: using list [-1] to preserve the time dim
+            self.last_loss = None
+
+        self.OUT.__setitem__('logits', logits)
+        self.OUT.__setitem__('last_loss', self.last_loss)
+
+        if use_kv_cache:
+            return self.OUT, past_kvs
+        return self.OUT
+
+
+    @torch.inference_mode()
+    def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=None, stream=True, repetition_penalty=1.):
+        index = idx.shape[1]
+        use_kv_cache = True
+        past_kvs = [None for _ in range(self.n_layers)]
+        while idx.shape[1] < max_new_tokens - 1:
+            # if the sequence context is growing too long we must crop it at block_size
+            idx_cond = idx  # if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
+            # forward the model to get the logits for the index in the sequence
+            inference_res = self(idx_cond, use_kv_cache=use_kv_cache, past_kvs=past_kvs)
+            if use_kv_cache:
+                logits, past_kvs = inference_res[0].logits, inference_res[1]
+            else:
+                logits = inference_res.logits
+
+            logits = logits[:, -1, :]  # crop to just the final time step
+
+            # Apply repetition penalty
+            for token in set(idx.tolist()[0]):
+                logits[:, token] /= repetition_penalty
+
+            if temperature == 0.0:
+                # "sample" the single most likely index
+                __, idx_next = torch.topk(logits, k=1, dim=-1)
+            else:
+                # pluck the logits at the final step and scale by desired temperature
+                logits = logits / temperature
+                # optionally crop the logits to only the top k options
+                if top_k is not None:
+                    v, __ = torch.topk(logits, min(top_k, logits.size(-1)))
+                    logits[logits < v[:, [-1]]] = -float('Inf')
+
+                # apply softmax to convert logits to (normalized) probabilities
+                probs = F.softmax(logits, dim=-1)
+                idx_next = torch.multinomial(probs, num_samples=1, generator=None)
+            # append sampled index to the running sequence and continue
+            if idx_next == eos:
+                break
+
+            idx = torch.cat((idx, idx_next), dim=1)
+            if stream:
+                yield idx[:, index:]
+
+        if not stream:
+            yield idx[:, index:]
+
+    @torch.inference_mode()
+    def eval_answer(self, idx):
+        # if the sequence context is growing too long we must crop it at block_size
+        idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
+        # forward the model to get the logits for the index in the sequence
+        past_kvs = [None for _ in range(self.n_layers)]
+        inference_res = self(idx_cond, use_kv_cache=False, past_kvs=past_kvs)
+        logits = inference_res.logits
+        logits = logits[:, -1, :]
+        return logits
+
+    def export(self, filepath='model.bin'):
+        """export the model weights in fp32 into .bin file to be read from C"""
+        f = open(filepath, 'wb')
+
+        def serialize(t):
+            d = t.detach().cpu().view(-1).numpy().astype(np.float32)
+            b = struct.pack(f'{len(d)}f', *d)
+            f.write(b)
+
+        # first write out the header
+        hidden_dim = self.layers[0].feed_forward.w1.weight.shape[0]
+        p = self.params
+        n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
+        header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
+                             n_kv_heads, p.vocab_size, p.max_seq_len)
+        f.write(header)
+
+        # next write out the embedding weights
+        serialize(self.tok_embeddings.weight)
+
+        # now all the layers
+        # attention weights
+        for layer in self.layers:
+            serialize(layer.attention_norm.weight)
+        for layer in self.layers:
+            serialize(layer.attention.wq.weight)
+        for layer in self.layers:
+            serialize(layer.attention.wk.weight)
+        for layer in self.layers:
+            serialize(layer.attention.wv.weight)
+        for layer in self.layers:
+            serialize(layer.attention.wo.weight)
+        # ffn weights
+        for layer in self.layers:
+            serialize(layer.ffn_norm.weight)
+        for layer in self.layers:
+            serialize(layer.feed_forward.w1.weight)
+        for layer in self.layers:
+            serialize(layer.feed_forward.w2.weight)
+        for layer in self.layers:
+            serialize(layer.feed_forward.w3.weight)
+        # final rmsnorm
+        serialize(self.norm.weight)
+        # note: no need to write final classifier weights due to weight sharing
+        # pos_cis
+        serialize(self.freqs_cos[:p.max_seq_len])
+        serialize(self.freqs_sin[:p.max_seq_len])
+
+        # write to binary file
+        f.close()
+        print(f"wrote {filepath}")

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39a113ff16b99a47a96737ff1b9957d6adc3e851aeb33c9115c52dcd11906c07
+size 435044370

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

@@ -0,0 +1,44 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<s>user\\n' + content + '</s>\\n<s>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '\\n' }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}