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Model: cococoomo/Exaone3.5-7.8B_ReST_V0_Quantized
Source: Original Platform

README.md

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+---
+language:
+- ko
+- en
+base_model:
+- LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
+pipeline_tag: text-generation
+tags:
+- llm
+- exaone
+- instruction-tuned
+- quantized
+- awq
+- vllm
+- medical
+---
+
+# Exaone3.5-7.8B_ReST_V0_Quantized
+
+This model is a fine-tuned and AWQ-quantized version of EXAONE 3.5 7.8B (Instruct), optimized for efficient inference and structured text generation.
+
+## Overview
+
+- Base Model: EXAONE 3.5 7.8B (Instruct)
+- Fine-tuning: Supervised fine-tuning on domain-specific data
+- Quantization: 4-bit AWQ
+- Inference: Optimized for vLLM
+- Context Length: up to 32K tokens
+
+## Model Details
+
+- Architecture: ExaoneForCausalLM  
+- Hidden Size: 4096  
+- Layers: 32  
+- Attention Heads: 32  
+- Max Position Embeddings: 32768  
+- Quantization: 4-bit AWQ  
+- Torch dtype: float16  
+
+## Intended Use
+
+- Instruction-based text generation  
+- Structured output generation (JSON)  
+- LLM-based data pipelines  
+- RAG systems  
+- Efficient inference  
+
+## Example Usage
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(
+    model="cococoomo/Exaone3.5-7.8B_ReST_V0_Quantized",
+    quantization="AWQ",
+)
+
+sampling_params = SamplingParams(
+    temperature=0.2,
+    top_p=0.8,
+    max_tokens=1024,
+)
+
+outputs = llm.generate(["Your prompt here"], sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+## Training
+
+Fine-tuned using supervised learning on domain-specific data.  
+Dataset is not included due to privacy constraints.
+
+## Limitations
+
+- May produce incorrect outputs  
+- Sensitive to prompt quality  
+- Domain bias may exist  
+
+## Safety
+
+Not intended for critical decision-making without human validation.
+
+## Evaluation
+
+- BLEU  
+- ROUGE  
+
+## Deployment
+
+Optimized for vLLM and GPU-efficient inference.