Upload CodeLlamaForCausalLM

config.json

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+{
+  "_name_or_path": "models/instruct_llama",
+  "architectures": [
+    "CodeLlamaForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "llama_model.CodeLlamaConfig",
+    "AutoModelForCausalLM": "llama_model.CodeLlamaForCausalLM"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.32.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.32.0"
+}

llama_model.py

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+from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaAttention, LlamaRotaryEmbedding
+from transformers.models.llama.configuration_llama import LlamaConfig
+import torch
+
+
+class CodeLlamaConfig(LlamaConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.rope_theta = 10000.0
+        if kwargs.get("rope_theta"):
+            try:
+                self.rope_theta = float(kwargs["rope_theta"])
+                print(f"Rope theta set to {self.rope_theta}")
+            except Exception:
+                print("Could not set rope theta properly, ensure it is a number")
+
+                
+class CodeLlamaNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+
+    def __init__(self, dim, max_position_embeddings=2048, base=1000000.0, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        self.base = base
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+class CodeLlamaForCausalLM(LlamaForCausalLM):
+    _tied_weights_keys = ["lm_head.weight"]
+    
+    config_class = CodeLlamaConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        for layer in self.model.layers:
+            attn = layer.self_attn
+            head_dim = attn.head_dim
+            max_embeddings = attn.max_position_embeddings
+            base = config.rope_theta
+                
+            attn.rotary_emb = CodeLlamaNTKScalingRotaryEmbedding(head_dim, max_embeddings, base=base)
+    
+    

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