End of training

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README.md

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+---
+tags:
+- generated_from_trainer
+model-index:
+- name: myBit-Llama2-jp-127M-test-12
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# myBit-Llama2-jp-127M-test-12
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 6.3033
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.00024
+- train_batch_size: 96
+- eval_batch_size: 96
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: polynomial
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 10.0642       | 0.07  | 20   | 9.4022          |
+| 9.0973        | 0.15  | 40   | 8.6900          |
+| 8.3576        | 0.22  | 60   | 7.9690          |
+| 7.7499        | 0.29  | 80   | 7.4678          |
+| 7.3343        | 0.36  | 100  | 7.1266          |
+| 7.0503        | 0.44  | 120  | 6.9132          |
+| 6.9035        | 0.51  | 140  | 6.8141          |
+| 6.8471        | 0.58  | 160  | 6.7611          |
+| 6.8217        | 0.65  | 180  | 6.7305          |
+| 6.7783        | 0.73  | 200  | 6.6345          |
+| 6.6382        | 0.8   | 220  | 6.4734          |
+| 6.5006        | 0.87  | 240  | 6.3855          |
+| 6.4282        | 0.95  | 260  | 6.3033          |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.38.2"
+}

model.safetensors

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+oid sha256:1442b89267cb6b4fc19a27bd580c9fe45f12e07bb48e7f7747edaa8da6cf47a9
 size 511344824

modeling_bit_llama.py

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+from typing import Optional
+from transformers.models.llama.modeling_llama import (
+    LlamaConfig, 
+    LlamaModel,
+    LlamaForCausalLM,
+    LlamaAttention,
+    LlamaFlashAttention2,
+    LlamaSdpaAttention,
+    LlamaMLP,
+    LlamaDecoderLayer,
+)
+from mybitnet.bitnet import BitLinear
+from torch import nn
+
+class BitLlamaConfig(LlamaConfig):
+    model_type = "bit_llama"
+    
+    def __init__(self, bits=8, **kwargs):
+        super().__init__(**kwargs)
+        self.bits = bits
+
+class BitLlamaMLP(LlamaMLP):
+    def __init__(self, config):
+        super().__init__(config)
+        self.gate_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, bits=config.bits, flg_before_linear=True)
+        self.up_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, bits=config.bits, flg_before_linear=True)
+        self.down_proj = BitLinear(self.intermediate_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=False)
+        
+class BitLlamaAttention(LlamaAttention):
+    def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
+        super().__init__(config)
+        self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=True)
+
+class BitLlamaFlashAttention2(LlamaFlashAttention2):
+    def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=True)
+
+class BitLlamaSdpaAttention(LlamaSdpaAttention):
+    def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
+        self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=True)
+
+BITLLAMA_ATTENTION_CLASSES = {
+    "eager": BitLlamaAttention,
+    "flash_attention_2": BitLlamaFlashAttention2,
+    "sdpa": BitLlamaSdpaAttention,
+}
+
+class BitLlamaDecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: BitLlamaConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.self_attn = BITLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+        self.mlp = BitLlamaMLP(config)
+
+class BitLlamaModel(LlamaModel):
+    def __init__(self, config: BitLlamaConfig):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [BitLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+class BitLlamaForCausalLM(LlamaForCausalLM):
+    def __init__(self, config: BitLlamaConfig):
+        super().__init__(config)
+        self.model = BitLlamaModel(config)
+        self.lm_head = BitLinear(config.hidden_size, config.vocab_size, bias=False, bits=config.bits, flg_before_linear=True)
+