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config.json

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+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 2048,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 2048,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32768
+}

convert_weight.py

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+import torch
+from tqdm import tqdm
+
+
+input_dir_path = "/scratch/project_462000086/norwegian_gpt/Megatron-DeepSpeed-fixed/mistral-7b-from-scratch-2nd-run/global_step30000"
+output_dir_path = "/scratch/project_462000086/norwegian_gpt/Megatron-DeepSpeed-fixed/hf_mistral_from_scratch_60k"
+
+n_hidden = 4096
+n_ffn_hidden = 14336
+n_heads = 32
+n_kv_heads = 8
+n_layers = 32
+n_tp = 2
+
+
+weights = {}
+
+# embedding
+embedding_weights = []
+for i in range(n_tp):
+    path = f"{input_dir_path}/layer_01-model_0{i}-model_states.pt"
+    checkpoint = torch.load(path)
+
+    embedding_weights.append(checkpoint["word_embeddings.weight"].bfloat16())
+
+weights[f"model.embed_tokens.weight"] = torch.cat(embedding_weights, dim=0)
+del embedding_weights
+
+lm_head_weights = []
+for i in range(n_tp):
+    path = f"{input_dir_path}/layer_{n_layers + 5}-model_0{i}-model_states.pt"
+    checkpoint = torch.load(path)
+
+    lm_head_weights.append(checkpoint["lm_head.weight"].bfloat16())
+
+weights[f"lm_head.weight"] = torch.cat(lm_head_weights, dim=0)
+del lm_head_weights
+
+
+# transformer layers
+for layer in tqdm(range(n_layers)):
+    q_weights, k_weights, v_weights, o_weights = [], [], [], []
+    up_weights, gate_weights, down_weights = [], [], []
+
+    for i in range(n_tp):
+        path = f"{input_dir_path}/layer_{layer+3:02d}-model_0{i}-model_states.pt"
+        checkpoint = torch.load(path)
+
+        weights[f"model.layers.{layer}.input_layernorm.weight"] = checkpoint["input_layernorm.weight"].bfloat16()
+        weights[f"model.layers.{layer}.post_attention_layernorm.weight"] = checkpoint["post_attention_layernorm.weight"].bfloat16()
+
+        kv_weight = checkpoint["self_attention.key_value.weight"].bfloat16()
+        k_weight, v_weight = torch.chunk(kv_weight, 2, dim=0)
+        k_weights.append(k_weight)
+        v_weights.append(v_weight)
+
+        q_weights.append(checkpoint["self_attention.query.weight"].bfloat16())
+        o_weights.append(checkpoint["self_attention.dense.weight"].bfloat16())
+        down_weights.append(checkpoint["mlp.dense_4h_to_h.weight"].bfloat16())
+
+        up_gate_weight = checkpoint["mlp.dense_h_to_4h.weight"].bfloat16()
+        up_weight, gate_weight = torch.chunk(up_gate_weight, 2, dim=0)
+        up_weights.append(up_weight)
+        gate_weights.append(gate_weight)
+
+    weights[f"model.layers.{layer}.self_attn.q_proj.weight"] = torch.cat(q_weights, dim=0)
+    weights[f"model.layers.{layer}.self_attn.k_proj.weight"] = torch.cat(k_weights, dim=0)
+    weights[f"model.layers.{layer}.self_attn.v_proj.weight"] = torch.cat(v_weights, dim=0)
+    weights[f"model.layers.{layer}.self_attn.o_proj.weight"] = torch.cat(o_weights, dim=1)
+    weights[f"model.layers.{layer}.mlp.up_proj.weight"] = torch.cat(up_weights, dim=0)
+    weights[f"model.layers.{layer}.mlp.gate_proj.weight"] = torch.cat(gate_weights, dim=0)
+    weights[f"model.layers.{layer}.mlp.down_proj.weight"] = torch.cat(down_weights, dim=1)
+
+
+# output layer norm
+path = f"{input_dir_path}/layer_{n_layers + 4}-model_00-model_states.pt"
+checkpoint = torch.load(path)
+
+weights[f"model.norm.weight"] = checkpoint["weight"].bfloat16()
+
+torch.save(weights, f"{output_dir_path}/pytorch_model.bin")

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9974eba4a7d380ce3a1710693dfa814512bcf2bd9360af3159da7b5a715475fc
+size 14496143545

special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "</s>"}

tokenizer_config.json

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+{"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "tokenizer_class": "PreTrainedTokenizerFast"}
+