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Exaone3.5-7.8B_ReST_V0_Quantized – AI Model by cococoomo | AlphaNeural AI

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cococoomo/
Exaone3.5-7.8B_ReST_V0_Quantized

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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.