base_model: Novaciano/BLAST_PROCESSING-3.2-1B
datasets:
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- anthracite-org/stheno-filtered-v1.1
- anthracite-org/nopm_claude_writing_fixed
- AiAF/SCPWiki-Archive-02-March-2025-Datasets
- passing2961/multifaceted-skill-of-mind
- huihui-ai/QWQ-LONGCOT-500K
- huihui-ai/LONGCOT-Refine-500K
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- alexandreteles/AlpacaToxicQA_ShareGPT
- Nitral-AI/Active_RP-ShareGPT
- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
- Nitral-AI/RP_Alignment-ShareGPT
- Chaser-cz/sonnet35-charcard-roleplay-sharegpt
- AiCloser/sharegpt_cot_dataset
- PJMixers/Gryphe_Opus-WritingPrompts-Story2Prompt-ShareGPT
- priveeai/pippa_sharegpt
- Locutusque/sharegpt_gpt4_uncensored_cleaned
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- microsoft/orca-agentinstruct-1M-v1
- microsoft/orca-math-word-problems-200k
- NousResearch/hermes-function-calling-v1
- AI-MO/NuminaMath-CoT
- AI-MO/NuminaMath-TIR
- allenai/tulu-3-sft-mixture
- cognitivecomputations/dolphin-coder
- HuggingFaceTB/smoltalk
- cognitivecomputations/samantha-data
- m-a-p/CodeFeedback-Filtered-Instruction
- m-a-p/Code-Feedback
- mlabonne/FineTome-100k
- PawanKrd/math-gpt-4o-200k
- V3N0M/Jenna-50K-Alpaca-Uncensored
- FreedomIntelligence/medical-o1-reasoning-SFT
language:
- es
- en
library_name: transformers
quantized_by: mradermacher
tags:
- transformers
- mergekit
- merge
- 1b
- rp
- nsfw
- roleplay
- español
- uncensored
- llama
- llama3.2
- not-for-all-audiences
About
weighted/imatrix quants of https://huggingface.co/Novaciano/BLAST_PROCESSING-3.2-1B
static quants are available at https://huggingface.co/mradermacher/BLAST_PROCESSING-3.2-1B-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | i1-IQ1_S | 0.6 | for the desperate |
| GGUF | i1-IQ1_M | 0.6 | mostly desperate |
| GGUF | i1-IQ2_XXS | 0.6 | |
| GGUF | i1-IQ2_XS | 0.7 | |
| GGUF | i1-IQ2_S | 0.7 | |
| GGUF | i1-IQ2_M | 0.7 | |
| GGUF | i1-Q2_K_S | 0.7 | very low quality |
| GGUF | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 0.8 | lower quality |
| GGUF | i1-IQ3_XS | 0.8 | |
| GGUF | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| GGUF | i1-IQ3_S | 0.9 | beats Q3_K* |
| GGUF | i1-IQ3_M | 0.9 | |
| GGUF | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 0.9 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 1.0 | |
| GGUF | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| GGUF | i1-Q4_0 | 1.0 | fast, low quality |
| GGUF | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 1.1 | fast, recommended |
| GGUF | i1-Q4_1 | 1.1 | |
| GGUF | i1-Q5_K_S | 1.2 | |
| GGUF | i1-Q5_K_M | 1.2 | |
| GGUF | i1-Q6_K | 1.3 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.