初始化项目，由ModelHub XC社区提供模型

Model: Bahasalab/Bahasa-4b-chat
Source: Original Platform

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README.md

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+---
+language:
+- id
+license: other
+license_name: tongyi-qianwen
+---
+
+# Bahasa-4b Model Report
+
+## Model Name
+**Bahasa-4b**
+
+## Model Detail
+Bahasa-4b is continued training from qwen-4b using 10 billion high quality text of Indonesian. The model outperforms some 4b, and even 7b models for Indonesian tasks.
+
+## Model Developers
+Bahasa AI
+
+## Intended Use
+This model is intended for various NLP tasks that require understanding and generating Indonesian language. It is suitable for applications such as question answering, sentiment analysis, document summarization, and more.
+
+## Training Data
+Bahasa-4b was trained on a 10 billion subset data of Indonesian dataset from a collected pool of 100 billion.
+
+## Benchmarks
+The following table shows the performance of Bahasa-4b compared to the models Sailor_4b and Mistral-7B-v0.1 across several benchmarks:
+
+| Dataset        | Version | Metric | Mode | Sailor_4b | Bahasa-4b-hf | Mistral-7B-v0.1 |
+|----------------|---------|--------|------|-----------|--------------|-----------------|
+| tydiqa-id      | 0e9309  | EM     | gen  | 53.98     | 55.04        | 63.54           |
+| tydiqa-id      | 0e9309  | F1     | gen  | 73.48     | 75.39        | 78.73           |
+| xcopa-id       | 36c11c  | EM     | ppl  | 69.2      | 73.2         | 62.40           |
+| xcopa-id       | 36c11c  | F1     | ppl  | 69.2      | 73.2         | -               |
+| m3exam-id-ppl  | ede415  | EM     | ppl  | 31.27     | 44.47        | 26.68           |
+| belebele-id-ppl| 7fe030  | EM     | ppl  | 41.33     | 42.33        | 41.33           |
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Bahasalab/Bahasa-4b-chat-v2",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Bahasalab/Bahasa-4b-chat")
+
+messages = [
+    {"role": "system", "content": "Kamu adalah asisten yang membantu"},
+    {"role": "user", "content": "kamu siapa"}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    input_ids=model_inputs.input_ids,
+    attention_mask=model_inputs.attention_mask,
+    max_new_tokens=512,
+    eos_token_id=tokenizer.eos_token_id
+
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+This data demonstrates that Bahasa-4b consistently outperforms the Sailor_4b model in various Indonesian language tasks, showing improvements in both EM (Exact Match) and F1 scores across different datasets, and is competitive with the Mistral-7B-v0.1 model.

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all_results.json

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+    "train_samples_per_second": 0.959,
+    "train_steps_per_second": 0.24
+}

config.json

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+  "_name_or_path": "/home/acul/data_nvme5/bahasa-4b/saves/Qwen1.5-4B/full/train_2024-03-27-23-212-110/checkpoint-210000",
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+  ],
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+  "model_type": "qwen2",
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+  "transformers_version": "4.39.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<|im_end|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
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+    "<|im_start|>",
+    "<|im_end|>"
+  ],
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+  "chat_template": "{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\\n' + system_message + '<|im_end|>\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}",
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+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

train_results.json

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+{
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+    "train_loss": 0.4881485020316365,
+    "train_runtime": 142779.1917,
+    "train_samples_per_second": 0.959,
+    "train_steps_per_second": 0.24
+}

training_args.bin

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