DavidAU/Qwen3-8B-256k-Context-8X-Grand

Qwen3-8B-256k-Context-8X-Grand

This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.

Qwen3 - 8B set at 256k (262144) context by extended YARN.

This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k.

By changing the maximum context (from 32k) to different values this changes:

• reasoning
• prose, sentence, and output
• general performance (up or down, depending on use case)
• longer and/or more detailed outputs, especially long form.

With the use of an Imatrix(es) this can further enhance model operation.

All versions of the model were:

• Imatrixed with NEO and HORROR datasets.
• Tested at IQ3_M (this is a much harder test than a higher quant)

All versions of the model at different context lengths worked, and passed basic reasoning, performance and reasoning tests.

Performance was solid across the board.

I suggest for best use:

• Minimum context length at 16k or higher, especially context models 128k and higher.
• Temp range of .5 to .7 for best reasoning, but much higher (1+, 2+, 3+) for creative use cases.
• For creative use cases, I found 64k and 96k very strong, but longer context version injected more details over long generation.
• For longer generations: Use longer, more detailed prompts for better overall generation, BUT short prompts work too.

For Quanting - GGUF:

• I suggest using an Imatrix and/or Imatrix versions of the model.
• For strongest reasoning: Q8 or full precision, however performance at IQ4XS/Q4 is still very strong.
• Set the output tensor to BF16, F16 or Q8 to improve reasoning performance.

In the files at this repo I have also included the RAW ".dat" files for NEO Imatrix and HORROR Imatrix that can be used to generate Imatrix quants. These can be used directly with "llama-quantize.exe" from LLamacpp.

If you want to quantize using "GGUF-MY-REPO" (with imatrix), I have included a SEVEN (7) raw text files ending with ".txt" and use these to make different imatrix version(s) of quants.

Imatrix quants IQ3s, IQ4s, and Q4s have the strongest imatrix effect.

If you want to quantize locally, you need to use the "llama-imatrix.exe" function to convert these text file(s) to ".dat" then you can use these to "imatrix" the quants / create imatrix quants.

You can use GGUF-MY-REPO and build standard quants without imatrix using this repo directly.

General Notes:

Max context on this version is : 256k (262,144)

Use Jinja Template or CHATML template.

Please refer the org model card for details, benchmarks, how to use, settings, turning reasoning on/off/ system roles etc etc :

[ https://huggingface.co/Qwen/Qwen3-8B ]

OPTIONAL SYSTEM ROLE:

You may or may not need this, as most times Qwen3s generate their own reasoning/thinking blocks.

You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.

See document "Maximizing-Model-Performance-All..." below for how to "set" system role in various LLM/AI apps below.

IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps).

This a "Class 1" (settings will enhance operation) model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

REASON:

Regardless of "model class" this document will detail methods to enhance operations.

If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub-par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for.

BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision):

This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model.

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

NOTE:

I strongly suggest you also visit the DavidAU GGUF (below) repo too for more details in using this model ; especially if it is "Class 3" or "Class 4" to get maximum performance from the model.

For full information about this model, including:

• Details about this model and its use case(s).
• Context limits
• Special usage notes / settings.
• Any model(s) used to create this model.
• Template(s) used to access/use this model.
• Example generation(s)
• GGUF quants of this model

Please go to:

[ GGUFS REPO coming soon || LEFT MENU under "Quantizations" ]

[[ model card updates to follow || GGUF repo(s) pending ... ]]