Model Gallery

Discover and install AI models from our curated collection

8 models available
1 repositories
Documentation

Find Your Perfect Model

Filter by Model Type

Browse by Tags

deepseek-v4.1-flash-ista-iq1_m
DeepSeek-V4.1-Flash-1017, a 763B-parameter mixture-of-experts model (384 experts per layer, 6 used per token), in ISTA-DAS's 1.75 bits per weight GSQ-RCO quantization (IQ1_M), the smallest size of the three (about 239 GB to download). It writes a reasoning trace before the answer, and the Strata backend splits it from the reply and parses the model's DSML tool calls. Text only: images are not supported by this backend. Needs a GPU with 12 GB of VRAM or more and about 34 GB of RAM at the least (Strata's figure; its measurements use 46 to 64 GB: an RTX 5070 with 64 GB answered at about 5 tokens per second, an RTX 3060 with 46 GB at 8 to 10). Less RAM still runs, slowly. Slightly lower quality than 2.00 bpw, and noticeably weaker on plain English prose; ISTA-DASLab measured 98.7% of the base model on its task average (AIME25, GPQA-Diamond, LiveCodeBench v6). Needs about 240 GB of free disk, on a fast NVMe drive: the 111 GB n-gram table (Engram) stays on disk and is read for every token. The Engram table and the 8 GB DSpark draft model are shared by all DeepSeek-V4.1 entries and downloaded once into strata/models/deepseek-v4.1-flash-shared. Runs only on the experimental strata backend, on NVIDIA CUDA 13 (x86_64 and arm64), AMD Radeon RX 9070 and R9700 (ROCm) and Apple Silicon (Metal). Strata ships no DeepSeek engine for CUDA 12, Intel or other AMD cards. On the first load the strata backend builds the Strata pack from the GGUF files (under a minute, a few MB of extra disk); loading the model afterwards takes a few minutes. The default context is 8192 tokens, the size Strata checked; raise context_size if the GPU has room. Licensed under the MIT License (DeepSeek-V4.1-Flash-1017 and the quantized files). The GGUF files are the GSQ-RCO quantization by ISTA-DAS (ISTA-DASLab); the DSpark draft model is Unsloth's; Strata is by Niko1221.

Repository: localaiLicense: mit

deepseek-v4.1-flash-ista-iq2_xxs
DeepSeek-V4.1-Flash-1017, a 763B-parameter mixture-of-experts model (384 experts per layer, 6 used per token), in ISTA-DAS's 2.00 bits per weight GSQ-RCO quantization (IQ2_XXS), the middle size of the three (about 257 GB to download). It writes a reasoning trace before the answer, and the Strata backend splits it from the reply and parses the model's DSML tool calls. Text only: images are not supported by this backend. Needs a GPU with 12 GB of VRAM or more and about 40 GB of RAM at the least (Strata's figure; its measurements use 46 to 64 GB: an RTX 5070 with 64 GB answered at about 5 tokens per second, an RTX 3060 with 46 GB at 8 to 10). Less RAM still runs, slowly. Strata measured a perplexity 15.5% above the original model on 4,995 positions (code 8.6%, agent work 5.3%). Needs about 258 GB of free disk, on a fast NVMe drive: the 111 GB n-gram table (Engram) stays on disk and is read for every token. The Engram table and the 8 GB DSpark draft model are shared by all DeepSeek-V4.1 entries and downloaded once into strata/models/deepseek-v4.1-flash-shared. Runs only on the experimental strata backend, on NVIDIA CUDA 13 (x86_64 and arm64), AMD Radeon RX 9070 and R9700 (ROCm) and Apple Silicon (Metal). Strata ships no DeepSeek engine for CUDA 12, Intel or other AMD cards. On the first load the strata backend builds the Strata pack from the GGUF files (under a minute, a few MB of extra disk); loading the model afterwards takes a few minutes. The default context is 8192 tokens, the size Strata checked; raise context_size if the GPU has room. Licensed under the MIT License (DeepSeek-V4.1-Flash-1017 and the quantized files). The GGUF files are the GSQ-RCO quantization by ISTA-DAS (ISTA-DASLab); the DSpark draft model is Unsloth's; Strata is by Niko1221.

Repository: localaiLicense: mit

deepseek-v4.1-flash-ista-iq2_xs
DeepSeek-V4.1-Flash-1017, a 763B-parameter mixture-of-experts model (384 experts per layer, 6 used per token), in ISTA-DAS's 2.25 bits per weight GSQ-RCO quantization (IQ2_XS), the largest size of the three (about 274 GB to download). It writes a reasoning trace before the answer, and the Strata backend splits it from the reply and parses the model's DSML tool calls. Text only: images are not supported by this backend. Needs a GPU with 12 GB of VRAM or more and about 44 GB of RAM at the least (Strata's figure; its measurements use 46 to 64 GB: an RTX 5070 with 64 GB answered at about 5 tokens per second, an RTX 3060 with 46 GB at 8 to 10). Less RAM still runs, slowly. Strata measured a perplexity 7.3% above the original model on 4,995 positions (code 3.2%, agent work 2.2%). Needs about 275 GB of free disk, on a fast NVMe drive: the 111 GB n-gram table (Engram) stays on disk and is read for every token. The Engram table and the 8 GB DSpark draft model are shared by all DeepSeek-V4.1 entries and downloaded once into strata/models/deepseek-v4.1-flash-shared. Runs only on the experimental strata backend, on NVIDIA CUDA 13 (x86_64 and arm64), AMD Radeon RX 9070 and R9700 (ROCm) and Apple Silicon (Metal). Strata ships no DeepSeek engine for CUDA 12, Intel or other AMD cards. On the first load the strata backend builds the Strata pack from the GGUF files (under a minute, a few MB of extra disk); loading the model afterwards takes a few minutes. The default context is 8192 tokens, the size Strata checked; raise context_size if the GPU has room. Licensed under the MIT License (DeepSeek-V4.1-Flash-1017 and the quantized files). The GGUF files are the GSQ-RCO quantization by ISTA-DAS (ISTA-DASLab); the DSpark draft model is Unsloth's; Strata is by Niko1221.

Repository: localaiLicense: mit

deepseek-v4.1-flash-ista-coder-iq2_xxs
ISTA-DAS's coding version of DeepSeek-V4.1-Flash-1017: in every layer 192 of the 384 routed experts are kept and the other 192 removed, stored in the 2.00 bpw (IQ2_XXS) layout. It is the smallest DeepSeek-V4.1 set here (about 191 GB to download). It writes a reasoning trace before the answer, and the Strata backend splits it from the reply and parses the model's DSML tool calls. Text only: images are not supported by this backend. Needs a GPU with 12 GB of VRAM or more and about 24 GB of RAM at the least (Strata's figure; its measurements use 46 to 64 GB: an RTX 5070 with 64 GB answered at about 5 tokens per second, an RTX 3060 with 46 GB at 8 to 10). Less RAM still runs, slowly. Strata measured a perplexity 48% above the original model on its test set (KL 0.40), so it is weaker than the full sets on prose and general knowledge; it is meant for code and agent work. Needs about 192 GB of free disk, on a fast NVMe drive: the 111 GB n-gram table (Engram) stays on disk and is read for every token. The Engram table and the 8 GB DSpark draft model are shared by all DeepSeek-V4.1 entries and downloaded once into strata/models/deepseek-v4.1-flash-shared. Runs only on the experimental strata backend, on NVIDIA CUDA 13 (x86_64 and arm64), AMD Radeon RX 9070 and R9700 (ROCm) and Apple Silicon (Metal). Strata ships no DeepSeek engine for CUDA 12, Intel or other AMD cards. On the first load the strata backend builds the Strata pack from the GGUF files (under a minute, a few MB of extra disk); loading the model afterwards takes a few minutes. The default context is 8192 tokens, the size Strata checked; raise context_size if the GPU has room. Licensed under the MIT License (DeepSeek-V4.1-Flash-1017 and the quantized files). The GGUF files are the GSQ-RCO quantization by ISTA-DAS (ISTA-DASLab); the DSpark draft model is Unsloth's; Strata is by Niko1221.

Repository: localaiLicense: mit

voxtral-mini-4b-realtime
Voxtral Mini 4B Realtime is a speech-to-text model from Mistral AI. It is a 4B parameter model optimized for fast, accurate audio transcription with low latency, making it ideal for real-time applications. The model uses the Voxtral architecture for efficient audio processing.

Repository: localaiLicense: apache-2.0

steelskull_l3.3-mokume-gane-r1-70b
Named after the Japanese metalworking technique 'Mokume-gane' (木目金), meaning 'wood grain metal', this model embodies the artistry of creating distinctive layered patterns through the careful mixing of different components. Just as Mokume-gane craftsmen blend various metals to create unique visual patterns, this model combines specialized AI components to generate creative and unexpected outputs.

Repository: localaiLicense: llama3.3

steelskull_l3.3-mokume-gane-r1-70b-v1.1
Named after the Japanese metalworking technique 'Mokume-gane' (木目金), meaning 'wood grain metal', this model embodies the artistry of creating distinctive layered patterns through the careful mixing of different components. Just as Mokume-gane craftsmen blend various metals to create unique visual patterns, this model combines specialized AI components to generate creative and unexpected outputs.

Repository: localaiLicense: llama3.3

steelskull_l3.3-san-mai-r1-70b
L3.3-San-Mai-R1-70b represents the foundational release in a three-part model series, followed by L3.3-Cu-Mai-R1-70b (Version A) and L3.3-Mokume-Gane-R1-70b (Version C). The name "San-Mai" draws inspiration from the Japanese bladesmithing technique of creating three-layer laminated composite metals, known for combining a hard cutting edge with a tougher spine - a metaphor for this model's balanced approach to AI capabilities. Built on a custom DeepSeek R1 Distill base (DS-Hydroblated-R1-v4.1), San-Mai-R1 integrates specialized components through the SCE merge method: EVA and EURYALE foundations for creative expression and scene comprehension Cirrus and Hanami elements for enhanced reasoning capabilities Anubis components for detailed scene description Negative_LLAMA integration for balanced perspective and response Core Capabilities As the OG model in the series, San-Mai-R1 serves as the gold standard and reliable baseline. User feedback consistently highlights its superior intelligence, coherence, and unique ability to provide deep character insights. Through proper prompting, the model demonstrates advanced reasoning capabilities and an "X-factor" that enables unprompted exploration of character inner thoughts and motivations.

Repository: localaiLicense: llama3.3