OrakzaiX

An 8B-class GGUF release for local, private inference

OrakzaiX is a community GGUF release maintained by Faisal Orakzai under the Orakzai ecosystem. The repository provides an 8B-class Llama-architecture model in multiple quantization levels for developers, researchers, and builders who want to run text generation locally on consumer hardware, workstations, edge devices, or private servers.

Important positioning: OrakzaiX is presented as a GGUF quantized model release. It is not represented as a newly trained foundation model unless separate training evidence and documentation are provided. Review the upstream model provenance and all applicable licenses before redistribution or commercial deployment.

Model summary

Property	Details
Model name	OrakzaiX
Model family	Llama architecture
Approximate parameter count	8B
Distribution format	GGUF
Primary task	Text generation and conversation
Recommended starting quantization	Q4_K_M
Maintainer and quantizer	Faisal Orakzai
Repository	FaisalOrakzai/OrakzaiX
Upstream metadata	meta-llama/Llama-3-8B-Instruct as supplied in the repository model card metadata

Intended use

OrakzaiX is intended for local experimentation, private assistants, offline prototyping, developer tooling, semantic workflow experiments, and research into multi-agent orchestration. Its local-first deployment model can help keep prompts and generated outputs within an operator-controlled environment, subject to the security of that environment and the selected runtime.

Potential application areas include:

• Local conversational assistants and knowledge-work prototypes.
• Structured workflow planning and task routing.
• Research summarization and technical information extraction.
• Code explanation, documentation assistance, and automation experiments.
• Private API services compatible with OpenAI-style client libraries.

The repository does not provide evidence of benchmark performance, domain-specific accuracy, tool-use reliability, or production-grade autonomy. Users should evaluate the exact quantization and prompt format against their own workloads.

Available GGUF files

All files are stored in this repository. File sizes below are the approximate repository sizes reported by Hugging Face.

Quantization	File	Approx. size	Typical use
Q2_K	autonomous_learner_1.Q2_K.gguf	3.18 GB	Lowest-memory experimentation
IQ3_XS	autonomous_learner_1.IQ3_XS.gguf	3.52 GB	Very low-memory inference
IQ3_S	autonomous_learner_1.IQ3_S.gguf	3.68 GB	Low-memory inference
Q3_K_S	autonomous_learner_1.Q3_K_S.gguf	3.66 GB	Low-memory inference
IQ3_M	autonomous_learner_1.IQ3_M.gguf	3.78 GB	Balanced low-memory option
Q3_K_M	autonomous_learner_1.Q3_K_M.gguf	4.02 GB	Balanced 3-bit option
Q3_K_L	autonomous_learner_1.Q3_K_L.gguf	4.32 GB	Higher-fidelity 3-bit option
IQ4_XS	autonomous_learner_1.IQ4_XS.gguf	4.48 GB	Compact 4-bit inference
Q4_K_S	autonomous_learner_1.Q4_K_S.gguf	4.69 GB	Compact 4-bit inference
IQ4_NL	autonomous_learner_1.IQ4_NL.gguf	4.71 GB	4-bit quality-focused option
Q4_0	autonomous_learner_1.Q4_0.gguf	4.66 GB	Broad runtime compatibility
Q4_K	autonomous_learner_1.Q4_K.gguf	4.92 GB	General 4-bit inference
Q4_K_M	autonomous_learner_1.Q4_K_M.gguf	4.92 GB	Recommended general starting point
Q4_1	autonomous_learner_1.Q4_1.gguf	5.13 GB	Alternative 4-bit format
Q5_K_S	autonomous_learner_1.Q5_K_S.gguf	5.60 GB	Higher quality with moderate memory
Q5_0	autonomous_learner_1.Q5_0.gguf	5.60 GB	Alternative 5-bit format
Q5_K	autonomous_learner_1.Q5_K.gguf	5.70 GB	Higher-fidelity local inference
Q5_K_M	autonomous_learner_1.Q5_K_M.gguf	5.73 GB	Balanced 5-bit option
Q5_1	autonomous_learner_1.Q5_1.gguf	6.07 GB	Alternative higher-fidelity format
Q6_K	autonomous_learner_1.Q6_K.gguf	6.60 GB	Quality-focused deployment
Q8_0	autonomous_learner_1.Q8_0.gguf	8.54 GB	Highest available quantized fidelity

Note: Actual RAM/VRAM requirements are higher than the file size in many deployments because the runtime also needs memory for the context window, KV cache, compute buffers, and operating-system overhead.

Which quantization should I choose?

Start with Q4_K_M for a practical balance between output quality, memory use, and speed. Choose Q3 or IQ3 variants when memory is constrained. Choose Q5_K_M, Q6_K, or Q8_0 when you have additional memory and want to reduce quantization loss. Larger context windows and GPU offloading can materially increase memory requirements regardless of quantization.

Quick start with llama.cpp

The examples below use the Hugging Face model identifier and the recommended Q4_K_M variant. Replace the quantization suffix when selecting another file.

Install or build llama.cpp

Use an official pre-built release or build from source by following the llama.cpp documentation. Recent releases support downloading GGUF files directly from Hugging Face.

Run a local command-line session

llama-cli -hf FaisalOrakzai/OrakzaiX:Q4_K_M

Start an OpenAI-compatible local server

llama-server -hf FaisalOrakzai/OrakzaiX:Q4_K_M \
  --host 127.0.0.1 \
  --port 8080

The server is then available at http://127.0.0.1:8080. Keep it bound to localhost unless you have explicitly configured authentication, TLS, network controls, and a trusted access boundary.

Run the Docker Model Runner example

docker model run hf.co/FaisalOrakzai/OrakzaiX:Q4_K_M

The exact command and supported options depend on the installed Docker Model Runner version.

Use with Python

For a local Python application, llama-cpp-python provides a commonly used GGUF runtime:

pip install llama-cpp-python

Download a specific GGUF file from this repository, then load it locally:

from llama_cpp import Llama

llm = Llama(
    model_path="./autonomous_learner_1.Q4_K_M.gguf",
    n_ctx=4096,
    verbose=False,
)

result = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Design a multi-agent routing workflow for a private fintech prototype."}
    ],
    temperature=0.7,
    max_tokens=512,
)

print(result["choices"][0]["message"]["content"])

Runtime parameters are workload-dependent. Increase n_ctx only when sufficient memory is available, and validate the chat template used by the selected runtime and model metadata before relying on instruction-following behavior.

Deployment guidance

For local use, begin with a 4-bit file and measure latency, prompt throughput, context capacity, and output quality on representative prompts. GPU offloading can improve performance, but it changes memory requirements and depends on the backend, driver, operating system, and hardware. For a shared or remotely accessible server, add authentication, TLS, rate limiting, request logging, resource quotas, and network isolation before exposing the endpoint beyond localhost.

For multi-agent systems, treat model output as untrusted text. Use schema validation, bounded retries, explicit tool permissions, deterministic post-processing, human review for consequential actions, and audit logs. Do not allow a model response to directly execute shell commands, transfer funds, modify production infrastructure, or publish content without an independent policy and authorization layer.

Limitations and responsible use

OrakzaiX may produce inaccurate, incomplete, biased, or unsafe content. Quantization may affect factuality, instruction following, formatting, and long-context behavior. Performance can vary by runtime, prompt template, sampling parameters, context length, language, and hardware.

Do not use this model as the sole basis for medical, legal, employment, financial, safety-critical, identity, or other high-impact decisions. Add qualified human review and domain-specific controls. Do not provide confidential, regulated, or personal information to a deployment unless the complete system has been reviewed and secured for that data.

The maintainer makes no claim that the model is suitable for every jurisdiction, industry, language, or production workload. Users are responsible for testing, monitoring, compliance, and all downstream use.

License and provenance

This repository declares license: other because the applicable rights depend on the upstream model, the quantization/conversion process, and any additional repository terms. Before commercial use, redistribution, or publication of outputs, review:

1. The OrakzaiX repository files and metadata.
2. The upstream model card and license for meta-llama/Llama-3-8B-Instruct.
3. The llama.cpp license and documentation, if using that runtime.
4. Any applicable laws, third-party licenses, usage policies, and organizational requirements.

This model card is documentation for the repository and does not grant additional rights beyond the applicable upstream and repository licenses.

Maintainer

Faisal Orakzai maintains the OrakzaiX repository under the Orakzai ecosystem.

• Website: faisalorakzai.com
• Hugging Face: @FaisalOrakzai

References