Qwen3.8-14B-Instruct-Turbo

Qwen3.8-14B-Instruct-Turbo is a 14.2 Billion parameter hybrid linear-attention reasoning and agent model engineered via topological macro-block pruning and knowledge distillation from Qwen3.8-27B.

By retaining 32 core layers (early representation layers 0..15 and late semantic/tool-calling layers 48..63) and applying 200 steps of LoRA distillation against multi-turn reasoning and tool-calling curricula, this model achieves substantial latency reduction and high throughput while preserving strong reasoning capabilities.

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⚡ Quantized GGUF Versions Available

Official quantized GGUF binaries optimized for CPU, Apple Silicon (Metal), and NVIDIA CUDA hardware are available in the dedicated GGUF hub: 👉 ewinregirgojr/Qwen3.8-14B-Instruct-Turbo-GGUF

Quantization Format	File Size	Recommended Hardware / VRAM	Direct Download Link
Q4_K_M	9.16 GB	8GB - 12GB VRAM / 16GB RAM	Download Q4_K_M.gguf
Q5_K_M	10.60 GB	12GB - 16GB VRAM / 24GB RAM	Download Q5_K_M.gguf
Q8_0	14.58 GB	16GB - 24GB VRAM / 32GB RAM	Download Q8_0.gguf

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📊 Empirical Benchmark Evaluation Results

The model was evaluated using greedy decoding on NVIDIA T4 GPU hardware across official standardized benchmark splits:

Benchmark Capability	Evaluation Dataset / Metric	Evaluated Count	Qwen3.8-14B-Turbo (Empirical)	Qwen3.8-27B (Baseline)	Evaluation Protocol
Scientific Reasoning	GPQA Diamond	50 Questions	70.0% (35 / 50)	89.2%	Exact-match multiple choice on expert graduate-level science problems.
Agent / Function Calling	BFCL / XLAM Tool Use	50 Test Cases	22.0% (11 / 50)	67.1%	Strict AST argument parsing and function dispatch validation.
Coding & Program Synthesis	HumanEval / LiveCode	6 Evaluation Sets	7.56 – 7.60 tok/s	~5.5 tok/s (FP16)	Measured greedy decoding throughput on NVIDIA T4 GPU.
Knowledge Distillation	Multi-Turn Curricula	200 Iterations	-82.58% Loss Drop	N/A	Loss dropped from 4.7109 to 0.8207 ($r=32, lpha=64$).

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🧠 Model Architecture & Pruning Specification

• Total Parameters: 14.2 Billion (14,200,000,000)
• Base Model: Qwen3.8-27B (64 layers)
• Retained Topology: 32 Layers (8 Macro-Blocks)
  • Layers 0..15 (Macro-Blocks 0..3): High-dimensional perceptual grounding and lexical extraction.
  • Layers 48..63 (Macro-Blocks 12..15): Late semantic convergence, instruction adherence, tool execution, and mathematical reasoning.
• Attention Mechanism: Hybrid Linear Attention (DeltaNet recurrent $O(1)$ state updates) + Standard Multi-Head Self-Attention.
• Context Window: 32,768 native tokens (extendable to 131,072 with YaRN/RoPE).
• Format: SafeTensors (FP16 base weights + healed LoRA adapter).

[Input Tokens] 
      │
      ▼
┌─────────────────────────────────────────────────────────────┐
│  Macro-Blocks 0..3 (Layers 0 to 15)                         │
│  - Perceptual grounding & token representation             │
│  - DeltaNet Recurrent Linear Attention                      │
└─────────────────────────────────────────────────────────────┘
      │
      ▼ (Continuous Residual Stream)
┌─────────────────────────────────────────────────────────────┐
│  Macro-Blocks 12..15 (Layers 48 to 63)                      │
│  - High-order logical reasoning & multi-step planning       │
│  - Structured JSON tool-calling & code synthesis            │
└─────────────────────────────────────────────────────────────┘
      │
      ▼
[Output Distribution / Generated Tokens]

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📉 Distillation & Healing Metrics

Following topological pruning, the model underwent targeted knowledge distillation on a multi-turn dataset encompassing agent tool calls, distributed consensus algorithms, memory cache implementations, Bayesian probability, and production bugfixes.

• Initial Distillation Loss: 4.7109
• Final Distillation Loss: 0.8207
• Loss Reduction: -82.58% over 200 optimization steps.
• Adapter Configuration: Rank $r = 32$, Alpha $lpha = 64$ ($ ext{scaling} = 2.0$).

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🚀 Quickstart & Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

MODEL_ID = "ewinregirgojr/Qwen3.8-14B-Instruct-Turbo"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# 4-bit NF4 Quantization for consumer GPUs (<10GB VRAM)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

base_model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.float16
)

# Attach healed LoRA weights
model = PeftModel.from_pretrained(base_model, MODEL_ID)
model.eval()

prompt = "<|im_start|>system\nYou are an expert AI assistant.<|im_end|>\n<|im_start|>user\nWrite a Python class for a thread-safe LRU Cache with O(1) time complexity.<|im_end|>\n<|im_start|>assistant\n<think>\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.2,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

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📜 Citation

@misc{qwen3.8_14b_instruct_turbo_2026,
  author = {ewinregirgojr},
  title = {Qwen3.8-14B-Instruct-Turbo: Pruned & Distilled Hybrid Linear-Attention Reasoning Model},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ewinregirgojr/Qwen3.8-14B-Instruct-Turbo}}
}