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bonsai-8b-1bit
Bonsai 8B (PrismML) is an end-to-end 1-bit language model built on the Qwen3-8B dense
architecture (GQA, SwiGLU, RoPE, RMSNorm, 36 layers, 65,536 context). Every weight is a
single sign bit (`-scale` / `+scale`) with one FP16 scale per group of 128 weights, for
an effective 1.125 bits/weight and a ~1.15 GB footprint (14.2x smaller than FP16) while
matching full-precision 8B instruct models at ~70.5 average across 6 benchmark
categories.
The Q1_0 quantization is only decodable by the PrismML llama.cpp fork, so this entry
runs on LocalAI's `bonsai` backend (that fork), not the stock `llama-cpp` backend.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-8b
Ternary Bonsai 8B (PrismML) is a 1.58-bit ternary language model on the Qwen3-8B dense
architecture. Each weight takes a value from {-1, 0, +1} with one shared FP16 scale per
group of 128 weights (GGUF Q2_0, ~2.18 GB deployed, 7.5x smaller than FP16). The extra
zero state recovers more of the full-precision model than the 1-bit build: it ranks 2nd
among compared 6-9B models at 75.5 average despite being ~1/8th their size.
Q2_0 is the recommended, ternary-lossless variant. The Q2_0 kernels are only in the
PrismML llama.cpp fork, so this runs on LocalAI's `bonsai` backend. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-8b-q2-g64
Ternary Bonsai 8B (PrismML), GGUF Q2_0 with group-64 packing (each FP16 scale shared
across 64 weights instead of 128). Slightly larger (~2.31 GB) but matches llama.cpp's
native 64-value Q2_0 block layout. Runs on LocalAI's `bonsai` backend. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-8b-pq2
Ternary Bonsai 8B (PrismML), GGUF PQ2_0 (packed Q2_0) ternary variant (~2.18 GB).
Same {-1, 0, +1} weight alphabet as Q2_0. Runs on LocalAI's `bonsai` backend.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
serenity-26b-a4b
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Repository: localaiLicense: apache-2.0
lfm2.5-8b-a1b
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# LFM2.5-8B-A1B
LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
- **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
- **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
- **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.
Find more information about LFM2.5-8B-A1B in our blog post.
**AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on Artificial Analysis.*
## 🗒️ Model Details
LFM2.5-8B-A1B is a general-purpose text-only model with the following features:
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Repository: localaiLicense: other
qwopus-glm-18b-merged
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwopus-glm-18b-merged
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwen_qwen3.5-0.8b
Qwen 3.5 0.8B parameter model quantized for llama-cpp backend. Supports chat interactions and multimodal image-text inputs.
Repository: localaiLicense: apache-2.0
qwen3-vl-embedding-8b
**Model Name:** Qwen3-VL-Embedding-8B
**Base Model:** Qwen/Qwen3-VL-8B-Instruct
**Description:**
The **Qwen3-VL-Embedding** and **Qwen3-VL-Reranker** model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities.
**Key Features:**
- Model Type: MultiModal Embedding
- Supported Languages: 30+ Languages
- Supported Input Modalities: Text, images, screenshots, videos, and arbitrary multimodal combinations (e.g., text + image, text + video)
- Number of Parameters: 8B
- Context Length: 32k
- Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 64 to 4096
**Downloads:**
- [GGUF Files](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) (e.g., `Qwen3-VL-Embedding-8B-Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_embedding import Qwen3VLEmbedder model = Qwen3VLEmbedder(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
qwen3-vl-reranker-8b
**Model Name:** Qwen3-VL-Reranker-8B
**Base Model:** Qwen/Qwen3-VL-Reranker-8B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-8B-GGUF) (e.g., `Qwen3-VL-Reranker-8B.Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
onerec-8b
The model `mradermacher/OneRec-8B-GGUF` is a quantized version of the base model `OpenOneRec/OneRec-8B`, a large language model designed for tasks like recommendations or content generation. It is optimized for efficiency with various quantization schemes (e.g., Q2_K, Q4_K, Q8_0) and available in multiple sizes (3.5–9.0 GB). The model uses the GGUF format and is licensed under Apache-2.0. Key features include:
- **Base Model**: `OpenOneRec/OneRec-8B` (a pre-trained language model for recommendations).
- **Quantization**: Supports multiple quantized variants (Q2_K, Q3_K, Q4_K, etc.), with the best quality for `Q4_K_S` and `Q8_0`.
- **Sizes**: Available in sizes ranging from 3.5 GB (Q2_K) to 9.0 GB (Q8_0), with faster speeds for lower-bit quantized versions.
- **Usage**: Compatible with GGUF files, suitable for deployment in applications requiring efficient model inference.
- **Licence**: Apache-2.0, available at [https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE](https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE).
For detailed specifications, refer to the [model page](https://hf.tst.eu/model#OneRec-8B-GGUF).
Repository: localaiLicense: apache-2.0
qwen3-vl-8b-instruct
Qwen3-VL-8B-Instruct is the 8B parameter model of the Qwen3-VL series.
Uses recommended default parameters according to Unsloth documentation for Qwen 3 VL.
Repository: localaiLicense: apache-2.0
qwen3-vl-8b-thinking
Qwen3-VL-8B-Thinking is the 8B parameter model of the Qwen3-VL series that is thinking.
Uses recommended default parameters according to Unsloth documentation for Qwen 3 VL.
Repository: localaiLicense: apache-2.0
opengvlab_internvl3_5-8b
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
Repository: localaiLicense: apache-2.0
opengvlab_internvl3_5-8b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
Repository: localaiLicense: apache-2.0
liquidai_lfm2-8b-a1b
LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
We're releasing the weights of our first MoE based on LFM2, with 8.3B total parameters and 1.5B active parameters.
LFM2-8B-A1B is the best on-device MoE in terms of both quality (comparable to 3-4B dense models) and speed (faster than Qwen3-1.7B).
Code and knowledge capabilities are significantly improved compared to LFM2-2.6B.
Quantized variants fit comfortably on high-end phones, tablets, and laptops.
Repository: localaiLicense: lfm1.0
qwen3-8b
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.
Model Overview
Qwen3-8B has the following features:
Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 8.2B
Number of Paramaters (Non-Embedding): 6.95B
Number of Layers: 36
Number of Attention Heads (GQA): 32 for Q and 8 for KV
Context Length: 32,768 natively and 131,072 tokens with YaRN.
Repository: localaiLicense: apache-2.0
mlabonne_qwen3-8b-abliterated
Qwen3-8B-abliterated is a 8B parameter model that is abliterated.
Repository: localaiLicense: apache-2.0
qwen3-8b-jailbroken
This jailbroken LLM is released strictly for academic research purposes in AI safety and model alignment studies. The author bears no responsibility for any misuse or harm resulting from the deployment of this model. Users must comply with all applicable laws and ethical guidelines when conducting research.
A jailbroken Qwen3-8B model using weight orthogonalization[1].
Implementation script: https://gist.github.com/cooperleong00/14d9304ba0a4b8dba91b60a873752d25
[1]: Arditi, Andy, et al. "Refusal in language models is mediated by a single direction." arXiv preprint arXiv:2406.11717 (2024).
Repository: localaiLicense: apache-2.0
josiefied-qwen3-8b-abliterated-v1
The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (“abliterated”) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities.
Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
These models are intended for advanced users who require unrestricted, high-performance language generation.
Introducing Josiefied-Qwen3-8B-abliterated-v1, a new addition to the JOSIEFIED family — fine-tuned with a focus on openness and instruction alignment.
Repository: localaiLicense: apache-2.0
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