DeepSeek-V4-Flash-IQ1_S-XL

DFQS SPECIFICATION v1.0

DeepSeek-V4-Flash-IQ1_S-XL (Reference Implementation)

284B MoE · 13B Active · 61.6GB GGUF · CPU-Feasible Inference

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Author: Darshani Persadh (@persadian)
Hugging Face Handle: @persadian
GitHub: arishma108
DOI: 10.57967/hf/8853
Publication Date: May 19, 2026

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ARTIFACT INTEGRITY

This section provides cryptographic verification of the DFQS-IQ1_S-XL artifact for reproducibility and integrity validation.

File: DeepSeek-V4-Flash-IQ1_S-XL.gguf (61.6GB)
SHA-256: b049d1eb34c068f19ab007b33c22a7d758b578bf2b10d9276e79654f85d35047
Timestamp: 2026-05-19 14:32:17 UTC

This hash verifies:

• file integrity
• deterministic reconstruction of the merged GGUF artifact
• consistency of DFQS-IQ1_S-XL deployment packaging

This block is intended for reproducibility validation across DFQS-compatible environments.

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1. SCOPE

This specification defines the DFQS (DeepSeek Flash Quantization Standard) for ultra-low-bit Mixture-of-Experts (MoE) deployment systems.

It defines:

• deployment constraints
• behavioral expectations
• evaluation interface
• reference implementation structure

This specification does NOT define:

• model training procedures
• fine-tuning workflows
• upstream architecture modifications

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2. TERMINOLOGY

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3. NORMATIVE REQUIREMENTS

SHALL

• DFQS-IQ1_S-XL SHALL support single-file GGUF execution
• Models SHALL operate in CPU-constrained environments
• Routing SHALL remain deterministic under standard inference loads

SHOULD

• Implementations SHOULD support llama.cpp runtime compatibility
• Evaluation SHOULD include long-context degradation analysis

MAY

• GPU acceleration MAY be used for optimization
• Extended context beyond 64K MAY be supported

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4. REFERENCE IMPLEMENTATION (IQ1_S-XL)

DFQS-IQ1_S-XL defines a constrained-memory MoE deployment class designed for:

• deterministic GGUF execution
• CPU-feasible inference
• ultra-low-bit routing stability
• single-file deployment architecture

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5. SPEC SNAPSHOT

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6. BEHAVIORAL CAPABILITIES (REFERENCE PROFILE)

7. ONE-LINE THESIS

DFQS-IQ1_S-XL defines an ultra-low-bit operational deployment class for large-scale MoE inference under constrained memory environments.

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8. DFQS POSITIONING LAYER

The following hierarchy defines DFQS-IQ1_S-XL within the broader inference compression spectrum:

FP16 / FP8 (Frontier Models)
→ Q4–Q6 GGUF (Production Inference)
→ IQ2 (Experimental Compression)
→ DFQS-IQ1_S-XL (Reference Implementation)

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9. WHY 61.6GB MATTERS

Traditional DeepSeek-V4-Flash deployments typically operate within:

• 120GB–300GB GGUF ranges
• GPU-first inference systems

DFQS-IQ1_S-XL establishes:

• sub-70GB operational envelope
• CPU-accessible MoE inference
• constrained-memory deployment feasibility

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10. BEHAVIORAL PROFILE

DFQS-IQ1_S-XL prioritizes operational stability under compression over benchmark maximization.

LIMITATIONS (BEHAVIORAL CONSTRAINTS)

• Performance degrades under long-context saturation
• Routing variance increases under extreme token pressure
• Memory constraints may trigger latency spikes or truncation behavior
• Inference stability is maintained within defined compression and memory constraints.

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11. EVALUATION INTERFACE

REQUIRED METRICS

All DFQS implementations SHALL report:

reasoning_score: float
code_score: float
context_stability_curve: list[float]
cpu_tokens_per_sec: float
failure_boundary_tokens: int

EVALUATION CONDITIONS

• CPU-only baseline unless specified
• llama.cpp runtime
• standardized prompt sets

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MEASUREMENT CONVENTION

All metrics MUST be reported under identical prompt and runtime conditions for cross-model comparability.

12. IMPLEMENTATION NOTES (NON-NORMATIVE)

The DFQS-IQ1_S-XL artifact uses a sequential shard merge process:

1. Sequential shard ingestion
2. Chunked binary concatenation
3. GGUF header validation
4. Post-validation cleanup

This describes implementation behavior and does not define DFQS requirements.

Efficiency Note

This approach reduces intermediate storage requirements compared to full shard reconstruction workflows.

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13. DEPLOYMENT

llama.cpp

# Using the merged single file
llama-server -hf persadian/DeepSeek-V4-Flash-IQ1_S-XL

# Or download and run locally
huggingface-cli download persadian/DeepSeek-V4-Flash-IQ1_S-XL DeepSeek-V4-Flash-IQ1_S-XL.gguf
./llama-cli -m DeepSeek-V4-Flash-IQ1_S-XL.gguf -p "Your prompt"

Python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="persadian/DeepSeek-V4-Flash-IQ1_S-XL",
    filename="DeepSeek-V4-Flash-IQ1_S-XL.gguf",
)

Ollama

ollama run hf.co/persadian/DeepSeek-V4-Flash-IQ1_S-XL

Docker

docker model run hf.co/persadian/DeepSeek-V4-Flash-IQ1_S-XL

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14. HARDWARE ENVELOPE

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15. VALIDATION STATUS

• GGUF integrity: validated at load-time
• Single-file structure: confirmed
• llama.cpp compatibility: tested
• CPU inference: operational

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15. SYSTEM ADOPTION ANALYSIS

The DFQS-IQ1_S-XL reference implementation has demonstrated substantial direct deployment adoption relative to the upstream shard-distribution workflow.

This adoption pattern suggests increasing preference toward:

• single-file deployment architectures
• constrained-memory inference workflows
• deployment-ready GGUF artifacts
• deterministic reconstruction-free execution paths

The separation between shard-based distribution and DFQS deployment implementation reflects a layered inference infrastructure model:

This repository serves as the canonical DFQS reference deployment implementation for DeepSeek-V4-Flash under constrained-memory operational environments.

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17. CITATION

@misc{persadian2026dfqs_iq1sxl,
  author = {Persadh, Darshani},
  title = {DFQS-IQ1_S-XL: Ultra-Low-Bit MoE Deployment Standard},
  year = {2026},
  publisher = {Hugging Face},
  version = {IQ1_S-XL},
  doi = {10.57967/hf/8853},
  url = {https://doi.org/10.57967/hf/8853}
}

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APA

Persadh, D.R. (2026). DFQS-IQ1_S-XL: Ultra-Low-Bit MoE Deployment Standard (IQ1_S-XL) [persadian/DeepSeek-V4-Flash-IQ1_S-XL.gguf]. Hugging Face. https://doi.org/10.57967/hf/8853

18. DFQS DEPLOYMENT EFFICIENCY CONTEXT

This model’s compression architecture reduces inference resource requirements relative to standard MoE deployments.

Carbon offset and reduced compute footprint are secondary outcomes of constrained-memory design.

Total CO2 offset: 20 kg · Offset Project Code: 9184338 This model is part of sustainable AI practices.

ENVIRONMENTAL IMPACT

This model's development and hosting have been carbon-offset through reforestation initiatives.

19. FINAL STATEMENT

This repository defines a DFQS-compliant deployment boundary for constrained Mixture-of-Experts inference systems.

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