Qwen3.8-2B-Empero-Q8_0-llamafile

Qwen3.8-2B Empero Q8_0 llamafile

An unofficial, local-only Empero-named llamafile package of empero-ai/Qwen3.8-2B-GGUF at revision f4f73582d0b149595450c719b9a7521a03894f9c. Empero AI is the developer and publisher of the distilled/fine-tuned model empero-ai/Qwen3.8-2B and the publisher of this direct quantized GGUF repository. This package embeds the existing Qwen3.8-2B-Q8_0.gguf bytes and adds the pinned llamafile 0.10.5 runtime plus launch defaults.

Complete model-tree chain: this Empero-named llamafile → empero-ai/Qwen3.8-2B-GGUF (direct quantized GGUF source published by Empero AI) @ f4f73582d0b149595450c719b9a7521a03894f9c → empero-ai/Qwen3.8-2B (Empero AI's full-parameter fine-tune/distill) @ e37a2dc4acc68ad75a91e07e63168cb04cc06345 → Qwen/Qwen3.5-2B (base).

This package is one derivative level below the Empero GGUF in the Hugging Face model tree: base_model points to empero-ai/Qwen3.8-2B-GGUF and base_model_relation is quantized. That relation records the direct quantized parent; the package embeds its existing Q8_0 bytes and did not itself re-quantize, train, distill, merge, fine-tune, or otherwise modify the model weights.

Empero AI claims:

Empero describes Qwen3.8-2B as a 2B-parameter full-parameter supervised fine-tune with off-policy distillation, trained on approximately 30,000 curated teacher traces.

Empero reports a 262,144-token native context and describes the model as the text path of a vision-language-model base. Empero says the fine-tune was text-only and that inherited vision capability was not evaluated.

These are publisher claims by Empero AI. This package has not independently validated the training method, data volume or curation, benchmark results, capabilities, native maximum context behavior, or inherited vision behavior. No benchmark or inference run was performed while building this package.

What this package is—and is not

This is packaging, not model development. The build copied the verified llamafile runtime, appended the pre-existing Q8_0 GGUF and .args as stored ZIP members, and verified byte identity and alignment. The builder did not train, fine-tune, distill, merge, convert, compress, re-quantize, edit, or load the model weights. The direct GGUF repository supplied the quantized file; this package does not claim authorship of that quantization.

The artifact is a native executable containing both code and model data. Treat it as executable software: obtain it from a trusted source, verify the SHA-256 below, review the provenance, and do not run altered copies.

Model and package details

Artifact identity

The artifact is below 4 GiB. That avoids the known size class that prevents direct Windows execution for larger llamafiles, but it does not prove Windows compatibility; Windows execution of this artifact is untested.

Components and provenance

The model entry begins at byte offset 350,814,208 and the .args entry at 2,427,518,976; both offsets are divisible by 65,536. Both entries are ZIP_STORED (uncompressed). The embedded model size and SHA-256 match the read-only source exactly.

Text-only package and projector status

The direct GGUF repository at the pinned revision provides no projector for this file. This package therefore does not download, embed, select, or enable a projector. Its .args contains no --mmproj option, and archive verification found exactly one embedded .gguf: Qwen3.8-2B-Q8_0.gguf.

Empero describes the source as the text path of a VLM base, says this fine-tune is text-only, and says inherited vision was not evaluated. Consequently, this package is documented and configured strictly for text generation. A VLM lineage label does not make this artifact image-capable: without a matching projector and validated preprocessing/client path, vision input is not enabled.

Packaged defaults

The exact retained and embedded .args is:

-m
/zip/Qwen3.8-2B-Q8_0.gguf
--ctx-size
8192
--jinja
--temp
0.6
--top-p
0.95
--top-k
20
--min-p
0.0
...

... passes later command-line arguments through to the runtime. Packaged defaults are an 8,192-token context, Jinja chat-template handling, temperature 0.6, top-p 0.95, top-k 20, and min-p 0.0. The 8,192-token package default is deliberately much smaller than Empero's publisher-reported 262,144-token native maximum to reduce baseline memory use.

Later arguments can override runtime options. For example:

./Qwen3.8-2B-Empero-Q8_0.llamafile --ctx-size 32768

A larger context can substantially increase memory use and may reduce speed. Support for a larger configured value also does not independently validate quality or stability at the publisher-reported maximum.

Terminal use

No execution was performed during packaging. After independently trusting and verifying the executable, a user may make it executable and inspect help:

chmod +x ./Qwen3.8-2B-Empero-Q8_0.llamafile
./Qwen3.8-2B-Empero-Q8_0.llamafile --help

A one-prompt text-generation invocation can be made with runtime arguments after the packaged defaults:

./Qwen3.8-2B-Empero-Q8_0.llamafile -p "Write a short explanation of byte alignment." -n 256

Local browser UI

Start the server explicitly on loopback:

./Qwen3.8-2B-Empero-Q8_0.llamafile --server --host 127.0.0.1 --port 8080

Then open:

http://127.0.0.1:8080/

Local API

The same server exposes a llama.cpp/llamafile API, including an OpenAI-compatible base commonly used as:

http://127.0.0.1:8080/v1

Client features can vary with the pinned runtime. Confirm endpoint behavior against llamafile 0.10.5 documentation rather than assuming parity with a hosted service.

Keep the server bound to 127.0.0.1 unless remote access is intentionally designed and protected. Binding to 0.0.0.0 or another non-loopback interface may expose prompts, outputs, and unauthenticated model endpoints to the local network. This package adds no authentication, TLS, firewall rules, or sandbox.

Portability and storage

The .llamafile is the single-file runnable package; the other files in this folder are reproducibility and audit records. It can be copied to a USB drive or portable SSD while preserving its filename and SHA-256. For reliable direct execution, use a filesystem and mount that support executable files and large files, preserve or restore the executable bit, and avoid running an untrusted or unexpectedly quarantined copy.

Portable media performance matters: first load and inference can be slower from a low-end flash drive than from an internal SSD. Copying between filesystems can also lose Unix permissions; re-run chmod +x on macOS/Linux after verifying the hash. On macOS, only remove a quarantine attribute if the file's origin and hash have been independently trusted.

Platform and RAM guidance

• macOS: packaged on arm64 macOS using the universal llamafile 0.10.5 runtime; this build was archive-verified but not executed.
• Linux: llamafile is designed for portable execution on supported Linux targets, but this exact artifact was not executed on Linux during this build.
• Windows: the artifact is below 4 GiB, so it is not in the known over-4-GiB direct-execution problem class. Direct Windows execution remains untested and should be described as unverified; users may need the filename presented as .exe depending on their workflow.
• Other systems: support depends on the runtime, CPU architecture, OS policy, filesystem, and available acceleration. Do not assume support from the APE format alone.
• Memory: the 1.93-GiB Q8_0 weights are only the baseline. Runtime state, KV cache, prompt length, and backend buffers add memory. For the packaged 8,192-token context, several additional GiB of available RAM is prudent; 6 GiB or more free is a conservative starting point, not a guarantee. Larger contexts need more.
• Acceleration: actual CPU/GPU offload, speed, and memory behavior depend on hardware and llamafile 0.10.5. No throughput or quality benchmark was run here.

Intended use

This package is intended for local, text-only experimentation, offline/portable demonstrations, command-line generation, and local UI/API integration by users who understand the resource and security implications of running a native executable. It is not a hosted endpoint, not a projector-enabled vision package, not an evaluation result, and not evidence that publisher benchmarks reproduce.

Evaluate outputs for the intended domain. The package inherits the model's possible hallucinations, bias, unsafe content, prompt-injection susceptibility, language variation, and factuality limits. Do not rely on it as the sole authority for medical, legal, financial, safety-critical, or other high-impact decisions.

Build and verification summary

The build performed these non-inference checks:

1. Required a previously nonexistent destination and sufficient free space.
2. Snapshotted the source-directory root and source stat before work.
3. Rejected symlinked inputs and verified source/runtime/zipalign sizes and SHA-256 hashes.
4. Copied the runtime, then invoked the matching hash-pinned zipalign-0.10.5 through /bin/zsh with -j0.
5. Verified ZIP CRC/integrity with Python and /usr/bin/unzip -tqq, unique member names, exact stored members, byte hashes, and 65,536-byte data alignment.
6. Verified the embedded and retained .args are byte-identical.
7. Re-hashed the read-only source and compared its stat and root snapshot after packaging.
8. Validated this card's deterministic frontmatter and attempted validation with huggingface_hub.ModelCard in an isolated temporary venv.
9. Removed temporary HOME/cache/TMPDIR/venv state and enforced the final root allowlist.

See BUILD-MANIFEST.md, SHA256SUMS.txt, and build-Qwen3.8-2B-Empero-Q8_0-llamafile.py for exact reproducibility evidence. The package was not loaded or run for inference. Nothing was uploaded or published during that packaging run; the later Hugging Face repository publication is a separate distribution step.

License and attribution

The direct model/GGUF metadata declares Apache License 2.0. A copy is included as LICENSE. Apache-2.0 governs the model according to its publishers; the bundled llamafile runtime and its compiled third-party components retain their upstream notices and terms. Review the direct source repositories and llamafile project when redistributing.

• GGUF source (published by Empero AI): https://huggingface.co/empero-ai/Qwen3.8-2B-GGUF
• Empero AI fine-tuned/distilled model: https://huggingface.co/empero-ai/Qwen3.8-2B
• Original base: https://huggingface.co/Qwen/Qwen3.5-2B
• llamafile project: https://github.com/Mozilla-Ocho/llamafile