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README.md

@@ -3,3 +3,63 @@ license: other
 license_name: hyperclovax-seed
 license_link: LICENSE
 ---
+
+## Overview
+
+HyperCLOVAX-SEED-Text-Instruct-0.5B is a Text-to-Text model with instruction-following capabilities that excels in understanding Korean language and culture. Compared to external competitors of similar scale, it demonstrates improved mathematical performance and a substantial enhancement in Korean language capability. The HyperCLOVAX-SEED-Text-Instruct-0.5B is currently the smallest model released by the HyperCLOVAX, representing a lightweight solution suitable for deployment in resource‑constrained environments such as edge devices. It supports a maximum context length of 4K and functions as a versatile small model applicable to a wide range of tasks. The total cost of a single training run for HyperCLOVAX-SEED-Text-Instruct-0.5B was 4.358K A100 GPU hours (approximately USD 6.537K), which is 39 times lower than the cost of training the `QWEN2.5‑0.5B‑instruct` model.
+
+
+## Basic Information
+
+- **Architecture**: Transformer‑based (Dense Model)  
+- **Parameters**: 0.57 B (total); 0.45 B (excluding token embeddings, tied embeddings)  
+- **Input/Output Format**: Text / Text  
+- **Maximum Context Length**: 4 K tokens  
+- **Knowledge Cutoff Date**: Trained on data up to January 2025
+
+
+## Training and Data
+
+The training dataset for HyperCLOVAX-SEED-Text-Instruct-0.5B consists of diverse sources, including the high‑quality data accumulated during the development of HyperCLOVAX-SEED-Text-Instruct-0.5B. Training was conducted in three main stages:
+1. **Pretraining**: Knowledge acquisition using high‑quality data and a high‑performance pretrained model.  
+2. **Rejection Sampling Fine‑Tuning (RFT)**: Enhancement of multi‑domain knowledge and complex reasoning capabilities.  
+3. **Supervised Fine‑Tuning (SFT)**: Improvement of instruction‑following proficiency.
+
+
+## Training Cost
+
+HyperCLOVAX-SEED-Text-Instruct-0.5B leveraged HyperCLOVA X’s lightweight training process and high‑quality data to achieve significantly lower training costs compared to industry‑leading competitors of similar scale. Excluding the SFT stage, a single pretraining run incurred:  
+
+| Pretraining Cost Category       | HyperCLOVAX-SEED-Text-Instruct-0.5B  | QWEN2.5‑0.5B‑instruct  |
+|---------------------------------|-----------------------------------------------|-------------------------------------|
+| **A100 GPU Hours**              | 4.358 K                                       | 169.257 K                           |
+| **Cost (USD)**                  | 6.537 K                                       | 253.886 K                           |
+
+This represents approximately a 39× reduction in pretraining cost relative to `QWEN2.5‑0.5B-instruct`.
+
+## Benchmarks
+
+| **Model** | **KMMLU (5-shot, acc)** | **HAE-RAE (5-shot, acc)** | **CLiCK (5-shot, acc)** | **KoBEST (5-shot, acc)** |
+| --- | --- | --- | --- | --- | 
+| HyperCLOVAX-SEED-Text-Base-0.5B | 0.4181 | 0.6370 | 0.5373 | 0.6963
+| HyperCLOVAX-SEED-Text-Instruct-0.5B | 0.3815 | 0.5619 | 0.4446 | 0.6299 | 
+| QWEN2.5-0.5B-instruct        | 0.2968 | 0.3428 | 0.3805 | 0.5025 | 
+
+## HuggingFace Usage Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("/path/to/HyperCLOVAX-SEED-Text-Instruct-0.5B")
+tokenizer = AutoTokenizer.from_pretrained("/path/to/HyperCLOVAX-SEED-Text-Instruct-0.5B")
+
+chat = [
+  {"role": "tool_list", "content": ""},
+  {"role": "system", "content": "- AI 언어모델의 이름은 \"CLOVA X\" 이며 네이버에서 만들었다.\n- 오늘은 2025년 04월 24일(목)이다."},
+  {"role": "user", "content": "슈뢰딩거 방정식과 양자역학의 관계를 최대한 자세히 알려줘."},
+]
+
+inputs = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_dict=True, return_tensors="pt")
+output_ids = model.generate(**inputs, max_length=1024, stop_strings=["<|endofturn|>", "<|stop|>"], tokenizer=tokenizer)
+print(tokenizer.batch_decode(output_ids))
+```
+

config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 100257,
+  "end_token_id": 100257,
+  "eos_token_id": 100257,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "pad_token_id": 100257,
+  "pretraining_tp": 1,
+  "resid_pdrop": 0.2,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.3",
+  "use_cache": false,
+  "vocab_size": 110592
+}

pytorch_model.bin

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special_tokens_map.json

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+}

tokenizer_config.json

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+}