language, base_model, pipeline_tag, tags

language	base_model	pipeline_tag	tags
ko en	LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct	text-generation	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

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.