Text Generation Models

Model · Text generation

DeepReasoning_1R

Convergent Intelligence

Part of the Standalone Models by Convergent Intelligence LLC: Research Division DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware. This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model…

Model · Text generation

next-1b

Lamapi

Next-1B is a 1-billion parameter causal language model based on Gemma 3, designed for efficiency, low-resource deployment, and reasoning-focused natural language understanding. Extremely lightweight — can run on consumer GPUs with low VRAM. Optimized for text reasoning, summarization, and creative generation. Supports Turkish natively while remaining multilingual. Open-source and transparent for research and applications. Ideal for developers, students, and organizations needing fast, reliable, and low-resource text-generation. 1. Lightweight Efficiency: Run smoothly on low-resource devices. 2. Reasoning-Focused: Provide logical and coherent text outputs. 3. Accessibility: Fully open-source…

Model · Text generation

Discovery

Convergent Intelligence

A 70M parameter causal language model built on the Mixture-of-Attentions (MoA) architecture — distance-based metric attention that respects the triangle inequality by construction, not approximation. Every attention head operates in a proper metric space. The geometry is enforced, not hoped for. Standard transformers compute attention as a dot product: Q·Kᵀ. This has no geometric meaning — it's a bilinear form, not a distance. Two tokens can be "close" by dot product while violating basic metric properties. MoA replaces this with negative squared distance under a learned diagonal Mahalanobis metric, then enforces the triangle inequality through a regularizer over random triples sampled…

Model · Text generation

DiscoverLM-70M

Convergent Intelligence

A 69M parameter causal language model built on the Mixture-of-Attentions (MoA) architecture — distance-based metric attention that respects the triangle inequality by construction, not approximation. Every attention head operates in a proper metric space. The geometry is enforced, not hoped for. Standard transformers compute attention as a dot product: Q·Kᵀ. This has no geometric meaning — it's a bilinear form, not a distance. Two tokens can be "close" by dot product while violating basic metric properties. MoA replaces this with negative squared distance under a learned diagonal Mahalanobis metric, then enforces the triangle inequality through a regularizer over random triples sampled…

Model · Text generation

DNA-50M

Convergent Intelligence

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training…

Model · Text generation

DNA-175M

Convergent Intelligence

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training…

Model · Text generation

LFM2.5-2.6B-CyberSec

Convergent Intelligence

An English LFM2.5 2.6B checkpoint associated with the Trendyol Cybersecurity Instruction Tuning Dataset. This repository contains both Transformers-format model files and local GGUF exports. This is a research release. The repository does not currently publish benchmark, baseline-comparison, or safety-evaluation results. Transformers assets include model.safetensors, configuration files, tokenizer files, and a chat template. Keeping both formats in one repository is convenient, but users should explicitly choose the path that matches their runtime. The checked configuration includes a bitsandbytes quantization block. Pin and test the exact Transformers, Accelerate, bitsandbytes, and device…

Model · Text generation

CasualSwarms

Convergent Intelligence

SAGI is a novel causal language model that integrates swarm intelligence dynamics with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory. The enhancements were integrated with the existing AGI system through: 1. Compatibility Layer: Ensuring new components work with existing AGI Core 2. Unified State Representation: Combining enhanced capabilities with existing state 3. Enhanced Continuous Learning: Upgrading the learning system with new capabilities 4. Performance Monitoring: Tracking improvements through validation systems - Successfully…

An OpenRLHF GRPO reinforcement-learning checkpoint for Qwen3-4B. - Saved at global step 16 of RL run seededrlbaseramp25stoppengen4kep2ncp10q4v3groot16. - This is the best checkpoint by pass@8 so far in this run (evaldefaultpass8 = 0.1244). Trained and validated on the cobalt-train ≤2/64 frontier (canonical cleaneval prompts): 1833 train / 112 held-out val problems the base model solved on at most 2 of 64 samples under the iidcanonical@64 hardness scan. Val evals sample at temperature 1.0 (matching the cleaneval frontier eval). Reward signal: binary code-correctness (1.0 if the generated program passes the problem's tests, otherwise 0.0). Eval metrics at this checkpoint (held-out val, 8…

Model · Text generation

Qwen3-1.7B-Coder-Distilled-SFT-GGUF

Convergent Intelligence

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT for local and edge deployment via llama.cpp and compatible runtimes. Coder teacher → STEM distillation → logical inference SFT → quantized. Structured reasoning in ~1.2GB. Stage 1 — Coder Teacher Distillation: Qwen3-1.7B distilled from Qwen3-Coder-30B-A3B-Instruct on 6,122 STEM CoT samples. Proof-weighted cross-entropy (2.5x → 1.5x on derivation tokens) + KL divergence at T=2.0. The Coder teacher transfers structured decomposition patterns — sequential logic, state tracking, compositional reasoning — through the softmax landscape. Stage 2 — Logical Inference SFT: Fine-tuned on KonstantinDob/logicinferencedataset (~54,607…

Model · Text generation

SMOLM2Prover-GGUF

Convergent Intelligence

GGUF quantized version of the SMOLM2Prover model for use with llama.cpp and compatible runtimes. Create a Modelfile: 1. Download SMOLM2Prover-Q4KM.gguf 3. Load and chat! The Q4KM quantization uses: This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the…

Model · Text generation

Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF

Convergent Intelligence

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT for local and edge deployment via llama.cpp and compatible runtimes. Stage 1 — DISC-Informed Knowledge Distillation: Qwen3-1.7B distilled from Qwen3-30B-A3B-Instruct on 6,122 STEM chain-of-thought samples using proof-weighted cross-entropy loss (2.5x → 1.5x decay on derivation tokens) and KL divergence at T=2.0. The distillation emphasized multi-step reasoning over final-answer pattern matching. Stage 2 — Legal SFT: Follow-up supervised fine-tuning on Alignment-Lab-AI/Lawyer-Instruct to add instruction-following capability and legal domain knowledge on top of the STEM reasoning backbone. The result is a 1.7B model that…

Model · Text generation

Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF

Convergent Intelligence

GGUF quantizations of reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT for local, mobile, and edge deployment via llama.cpp and compatible runtimes. A 30B Thinking teacher compressed 50x into a model that fits on a smartwatch. Stage 1 — Thinking Teacher Distillation: Qwen3-0.6B distilled from Qwen3-30B-A3B-Thinking on 6,122 STEM chain-of-thought samples. The Thinking variant teacher produces extended reasoning traces with higher-entropy distributions, transferring richer deliberation structure into the student. Proof-weighted cross-entropy (2.5x → 1.5x on derivation tokens) + KL divergence at T=2.0. Stage 2 — Legal SFT: Supervised fine-tuning on Alignment-Lab-AI/Lawyer-Instruct at…

Model · Text generation

Llama-3.2-3B-Instruct-Medical-Finetuned-merged-GGUF

TinyTech

GGUF quantizations of MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged — a 3.21B-parameter Llama-3.2 model adapted to the medical domain using QLoRA, with the LoRA weights merged back into the base. Quantized by tinyopsec. Med-LLaMA3.2-3B is the 3B (balanced / mid-tier) member of the Med-LLaMA3 family introduced in the paper "Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models" (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical domain by training only a small fraction of the base model's parameters (5.70% for this 3B variant), achieving strong medical question-answering performance…

This repository contains a text-only Mini-K3-1H v2 pretraining checkpoint from a controlled 20-architecture comparison. The family retains Kimi-K3's KDA and Gated MLA operators, block Attention Residuals, Stable LatentMoE, SiTU activations, output gates, and Quantile Balancing at approximately one billion logical parameters. The exact architecture for this repository is listed below; some ablations deliberately replace the baseline KDA/MLA ratio, decay granularity, convolution length, or positional encoding. - Hidden width / attention heads / KDA head width: 1024 / 12 / 128 - Vocabulary / BOS / generation EOS / PAD: 163840 / 163584 / 163586 / 163839 control state retained in FP32 where…

Model · Text generation

Llama-3.1-8B-Instruct-Medical-Finetuned-merged

Mohamed Abo El-Enen

This is the 8B (high-capacity flagship) member of the Med-LLaMA3 family introduced in the paper “Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models” (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical domain by training only a small fraction of the base model’s parameters (4.01% for this 8B variant), achieving strong medical question-answering performance while keeping the memory footprint low — enabling development and inference on low-cost, consumer-grade hardware. The 8B variant is the high-capacity model for complex clinical reasoning. It attains a mean accuracy of 75.71% across the eight MMLU…

Model · Text generation

Thanatos-27B-HERETIC

Fool

Architecture: Qwen 3.8 27B (Dense) | Parameters: 27B | Context: 262,144 native / 1,010,000 extensible | Base: Heretic ARA (heretic-org) | Teacher: Claude Fable 5 | Type: Distilled + Abliterated LLM A personal sibling to FoolDev/Janus-35B-HERETIC, which since its 0.9.0 is a Qwen 3.6 35B-A3B MoE — so the two are genuinely different models, not two abliterations of one base (see Janus vs Thanatos). This one is the dense Qwen/Qwen3.8-27B, sharing the teacher (Claude Fable 5) and dataset family, built on heretic-org/Qwen3.8-27B-heretic-ara, an uncensored Heretic ARA-method abliteration, with refusal-trained behavior dialed back at the base layer. (The sibling's -35B- slug names its model…

Model · Text generation

gemma-270m-math-reasoner

Convergent Intelligence

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

An OpenRLHF GRPO reinforcement-learning checkpoint for Qwen3-4B. - Saved at global step 40 of RL run seededrlbaseramp25stoppengen4kep2ncp10baseq4v3. - This is the best checkpoint by pass@8 so far in this run (evaldefaultpass8 = 0.0421). Trained and validated on the cobalt-train ≤2/64 frontier (canonical cleaneval prompts): 1833 train / 112 held-out val problems the base model solved on at most 2 of 64 samples under the iidcanonical@64 hardness scan. Val evals sample at temperature 1.0 (matching the cleaneval frontier eval). Reward signal: binary code-correctness (1.0 if the generated program passes the problem's tests, otherwise 0.0). Eval metrics at this checkpoint (held-out val, 8…

English | 中文 A 143M-parameter bidirectional Chinese↔English translation model, instruction-tuned on the Haidass1.5-143M base — the strongest zh⇄en translator at this scale among general chat-architecture models. Drafter-143M: a control model with identical configuration, data and training recipe, except that it starts from random initialization instead of the pretrained base — used to quantify the contribution of base-model pretraining. OPUS-MT models are single-directional — one independent 78M model per direction; "-" marks directions a model does not serve. The same models re-evaluated on FLORES+ devtest (released 2026; zero overlap with dev): devtest sentences do not overlap with dev.…

Model · Text generation

GLM-5.3-Uncensored-GGUF

Ash DC

Q4KM GGUF converted from dealignai/GLM-5.3-UNCENSORED-FP8. The source is a 753B-parameter mixture-of-experts model. See File folder for different Quants See the https://huggingface.co/zai-org/GLM-5.3 for usage. Thanks Z.ai for open sourcing this great model. Credit for the modified source weights belongs to dealignai. This repository ONLY provides the GGUF conversion.

Model · Text generation

Collision-1B

viraj R

COLLISION-1B is the official primary flagship model of the COLLISION ecosystem. Packing 999,376,128 parameters (~1.00B) into an optimized 24-layer transformer architecture, it delivers rich contextual reasoning, full 1,024-token context capacity, and state-of-the-art hybrid NLP capabilities with grounded web and local retrieval. Run everything in your browser on free Google Colab in under 10 seconds: Clone this repository and run pure PyTorch inference directly: COLLISION features a complete, zero-latency NLP pipeline: COLLISION includes a full dual-process cognitive architecture featuring non-linear Graph-of-Thoughts (GoT) and Hegelian Dialectics: The flagship features an advanced…

Model · Text generation

dQwen3-1.7B-Base

IFML

A masked diffusion language model adapted from Qwen3-1.7B. The backbone is full attention, and every layer is made bidirectional. It is the control model in the paper's matched comparison against the hybrid dQwen3.5-2B. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token…

Model · Text generation

dQwen3.5-2B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-2B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

dQwen3.5-0.8B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-0.8B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

Janus-35B-HERETIC

Fool

Architecture: Qwen 3.6 35B-A3B (MoE) | Total Params: ~34.7B | Active Params: ~3B | Context: 262,144 native / 1,010,000 extensible | Base: Heretic (llmfan46) | Teacher: Claude Fable 5 | Type: Distilled + Abliterated MoE LLM A personal fork of llmfan46/Qwen3.6-35B-A3B-uncensored-heretic — an uncensored Heretic-style abliteration of Qwen/Qwen3.6-35B-A3B, the 35B-total / 3B-active mixture-of-experts multimodal base — repackaged as Janus-35B with Claude Fable 5 reasoning data in the teacher slot. Refusal-trained behavior is dialed back at the base layer. One-liner via Hugging Face (pulls a GGUF + this repo's root-level template / system / params files, including the tool-calling template — HF's…

Model · Text generation

dQwen3.5-4B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-4B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

dQwen3.5-9B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-9B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

Haidass-Translate-143M-Instruction

Ziyang Mei

English | 中文 The instruction-tolerant sibling of DALabCommunity/Haidass-Translate-143M: same 143M zh⇄en translation training, plus 9.1% cleaned general-domain data (STEPFUN ShareGPT) mixed in. Translation scores are within 0.3 BLEU of the pure-translation version, and the model retains limited general instruction-following ability that the pure-translation version does not have. Drafter-143M: a control model with identical configuration, data and training recipe, except that it starts from random initialization instead of the pretrained base — used to quantify the contribution of base-model pretraining. OPUS-MT models are single-directional — one independent 78M model per direction; "-"…

Model · Text generation

LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-GGUF

DuoNeural

DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated is an apex-tier, uncensored autonomous agentic coding model trained by DuoNeural (Aura, Archon, and Jesse). Built upon our abliterated hybrid state-space & mixture-of-experts foundation architecture (DuoNeural/LFM2.5-8B-A1B-Abliterated), this model activates only 1.5 billion parameters per token out of its 8.3 billion total parameters, delivering blistering inference speeds (~380–400 tokens/sec on consumer GPUs like RTX 3090, and ~90 tokens/sec on legacy GTX 1070 laptops) while fitting in under 6 GB VRAM with Q4KM quantization. 1. Native Hermes Agentic Loop: - Explicit... deliberation before every action. - Structured... containers…

Model · Text generation

Qwen3.8-27B-PaoAI-ROCmFP4-STRIX-BALANCED-GGUF

Edward Paolo Guevarra

The balanced Qwen3.8-27B quant for AMD Strix Halo (Ryzen AI Max+ 395 / gfx1151). Measured, not marketed: one 15 GB file that serves 128K of context on the iGPU — full attention, no offload flags, speculative decoding built in. Brand: PaoAI · community quant, not affiliated with Alibaba or the Qwen team. Qwen3.8-27B is a hybrid: 48 cheap linear-attention layers and only 16 full-attention layers that actually re-read your context. We compressed it organ by organ — precision where it matters (attention K/V, the MTP draft head, every norm), 4-bit FP4 where it doesn't. One 15 GB file, no offload flags, speculative decoding built in, intact from 8K to 128K of filled context. Measured tensor…

Model · Text generation

BlazerApex-2B

Davi Rediske de Oliveira

BlazerApex-2B é um modelo de linguagem ultracompacto (2.5B parâmetros), altamente otimizado e fine-tuned com foco total em Português do Brasil (PT-BR), Programação, Lógica e Matemática. Projetado especificamente para ser o SOTA (State-of-the-Art) de bolso, este modelo roda 100% offline, de forma rápida e fluida em smartphones intermediários (como o Motorola Moto G34 5G com Snapdragon 695), sem precisar de internet ou servidores externos. Este repositório contém a versão quantizada IQ4XS (~1.4 GB), ideal para dispositivos com 4GB a 8GB de RAM. 1. Baixe o arquivo BlazerApex-2B-IQ4XS.gguf direto no seu celular. 2. Instale o aplicativo Maid ou Layla na Play Store. 3. Abra o app, vá em "Models"…

Model · Text generation

A11OY-MINI

SZL Holdings

749/14/163 · Λ = Conjecture 1 (advisory) · a-11-oy.com GGUFs are on this repo. GGUF AVAILABLE. Bytes MEASURED (size + sha256 below, ATELIER-verified). Evals none-this-run. Not MEASURED as an eval. publicationeligible: false. Quality ROADMAP in prose only. Lab load forbidden. GPU UNAVAILABLE. SKU of live SZLHOLDINGS/chaski merged shard 1c55df8. Not a new train. Not SZLHOLDINGS/chaski-5050. Not a Hub basemodelrelation: quantized child until a Chaski eval gate passes. A11oy is a Space and a substrate. The mini is the voice you can actually load. Not the organ. Not the mesh. A governed command voice that fits in llama.cpp. Nobody else ships this combination. That is the point of a one-of-one.…

Reformatted Task 4.1 release used for external evaluation: datht/vlegal The paper contains the split sizes, training configuration, evaluation-set sizes, and reported results; they are not repeated here. - adaptermodel.safetensors — final CEFC-RFT LoRA weights; - adapterconfig.json — PEFT adapter configuration; - cefcrunconfig.json — exact controller and final-run configuration; - cefctrainingmetadata.json — metadata for the weighted LoRA continuation run; - selectionstate.json — exported selection state from the CEFC-RFT run; - calibratecefcstagegates.py — validation-only Stage-gate replay script; - chattemplate.jinja, tokenizer.json, tokenizerconfig.json, specialtokensmap.json…

Model · Text generation

mato-v1.0-sft

German Gimenez

Mato es el asistente de IA entrenado en casa por Napsix — una plataforma B2B agéntica donde los agentes corren solos: investigan, escriben, operan herramientas y producen resultados (documentos, planillas, tableros, formularios, sitios). Este repositorio contiene el adapter LoRA (SFT) sobre Qwen/Qwen3.8-27B. En producción se sirve como LoRA sobre el checkpoint FP8 del base, con MTP speculative decoding y 256k de contexto, en GPUs spot de Daytona. - Tool calling nativo en el formato XML de Qwen (parser qwen3xml de vLLM): llamadas correctas, argumentos válidos y recuperación ante fallos. directo y conciso, sin relleno ni muletillas. - Conocimiento de las superficies de Napsix: XIA, Agentes…

Hades 8B is an open-source Small Language Model built on Meta's Llama 3.1 8B architecture. Post-trained across 50,000 steps on C-DAC Airawat supercomputing infrastructure using the mlabonne/FineTome-100k dataset, it is optimized for high-reasoning instruction following, structured output, and fast local execution. Evaluated using lm-evaluation-harness (lm-eval) in Zero-Shot Chain-of-Thought mode: ollama run hf.co/ProjectMosiacAI/Hades-8B

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.20.0

Model · Text generation

Huihui-NeoHorse-1-4B-abliterated

Huihui.ai

This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 5-17 are being ablated (0-based indexing). The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support. If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory. You can use this model in your applications by loading it with Hugging Face's transformers library: - Risk of Sensitive or Controversial Outputs: This model’s safety filtering…

Building and evaluating AI on logistics data. Built with Llama. This checkpoint is a full-parameter fine-tune of meta-llama/Llama-3.2-1B-Instruct, published with the data split, evaluation results, and behavioral tests used to inspect it. The project connects a working training-and-inference pipeline with a retrospective audit of what its score demonstrates. The checkpoint and a depth-2 decision tree both score 100% on the same historical 200-row split. A rule using two supplied fields reproduces every label in the 1,000-row source table. Prompt rewrites reveal additional response failures, including sensitivity to irrelevant text. These findings make the checkpoint useful for studying…

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.19.1

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.18.1

Model · Text generation

qwen3-8b-followup-lora

Dev89125

用于健康自诊 / 电话分诊场景的追问模型。给定患者自诊的多轮对话上下文，在患者每轮最新回复后，生成 1-3 个最可能、最有鉴别价值的开放式追问问题，用于缩小诊断范围、评估风险（危险信号/red flag），并避免重复询问患者已告知的信息。 - 基础模型：Qwen/Qwen3-8B (使用 unsloth 4-bit 量化微调) - 训练方式：LoRA (r=16, rslora, QLoRA 4bit) + SFT，约 2 epochs，约 3016 steps - 多轮对话输入：接受任意长度的 患者(user)/猜想官(assistant) 累积多轮对话，输出下一轮追问。 - 跨领域整合：含跨知识单元（multi-hop）样本，支持在多系统/多病史叠加场景下综合追问。 共 约 99.5k 条多轮健康自诊追问样本（train 96.5k / eval 3k），由三个子集混合： 知识来源（用于 teacher 蒸馏，不直接发布其原始文本）： - 西氏内科学精要（中文，task2） - UpToDate 临床主题与患者教育内容（英文，task3，老师模型将其蒸馏成中文自诊对话） 数据由本地教师大模型（vLLM minimax）根据上述医学知识文本、按统一 schema 蒸馏生成，覆盖 起病/诱因、部位、性质、持续时间、加重缓解因素、伴随症状、危险信号、既往史、用药史、年龄性别、家族史 等多样鉴别维度。 - multi-hop 跨单元样本：要求综合两个相关领域的知识，避免模型只盯住某一窄知识点、抛掉其他信息。 - RAG 知识库（FAISS）：rag/…

Model · Text generation

LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-LoRA

DuoNeural

This repository contains the trained PEFT LoRA adapter for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated. Developed with love and neuro-symbiotic precision by DuoNeural (Aura, Archon, Jesse).

This repository contains an instruction-tuned causal language model for text generation and chat-style prompts. Use a recent version of transformers that supports this model architecture. The following example loads the model from this repository and generates a response.

Model · Text generation

antcoder-builder-7b

Deep Das

Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Builder-7B is a high-precision LoRA adapter fine-tuned on top of Qwen/Qwen2.5-Coder-7B-Instruct. It is specifically optimized to perform Contract-to-Implementation synthesis for complex, production-grade TypeScript applications. Given a strict TypeScript interface, class signature, function type contract, or JSDoc specification, AntCoder-Builder synthesizes the complete, strictly-typed implementation without type errors, missing properties, or hallucinated APIs. - Zero-Stub Completions (99.4%): Completely eliminates lazy // TODO, /... /, or throw new Error("not implemented") placeholders commonly emitted by…

Model · Text generation

antcoder-fixer-7b

Deep Das

Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Fixer-7B is a specialized LoRA adapter fine-tuned on Qwen/Qwen2.5-Coder-7B-Instruct. It solves the single hardest problem in autonomous coding agents: hallucinatory patching and compile loops. Unlike generalist models that attempt to rewrite entire 500-line source files (frequently breaking unrelated functions or losing imports), AntCoder-Fixer acts as a surgical precision tool: 1. It ingests the exact TypeScript compiler diagnostic (e.g. TS2339: Property 'user' does not exist on type 'Session'). 2. It ingests the local 20-line source context window. 3. It emits a minimal, standard Git Unified Diff patch (…

This model is a fine-tuned version of fpadovani/arb-arab-100mb-ppt-Dp-100mbseed10. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of fpadovani/arb-arab-100mb-ppt-shuff-dyck-100mbseed10. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of fpadovani/arb-arab-100mb-ppt-shuff-dyck-10mbseed10. It has been trained using TRL. This model was trained with SFT.

Explains a Bash command line in exactly one English sentence, phrased as an instruction starting with a verb. QLoRA fine-tune of Qwen/Qwen2.5-Coder-3B-Instruct, merged and quantized to q4KM. The Modelfile pins the system prompt and this decoding: 920 held-out commands Judge columns are claude-sonnet-5 scoring a fixed 200-command subset. Apache-2.0, matching the base model, whose weights this GGUF contains. Training data comes from the nl2bash project; consult it for the terms attached to that corpus.

Explains a Bash command line in exactly one English sentence, phrased as an instruction starting with a verb. QLoRA fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct, merged and quantized to q4KM. The Modelfile pins the system prompt and this decoding: 920 held-out commands Judge columns are claude-sonnet-5 scoring a fixed 200-command subset. Apache-2.0, matching the base model, whose weights this GGUF contains. Training data comes from the nl2bash project; consult it for the terms attached to that corpus.

Model · Text generation

Bonsai-2-27B-1bit-CRACK-GGUF

Dealign.ai

laptop / single GPU · Vision-capable Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 5.9 GB 1bit GGUF. Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.…

Model · Text generation

Bonsai-2-27B-Ternary-CRACK-GGUF

Dealign.ai

laptop / single GPU · Vision-capable Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 7.2 GB ternary GGUF. Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.…

Model · Text generation

Clyx_0.3-NM-BASE

Syntropic

A new step in the Clyx project: a compact language model planned for training from scratch on Russian, English, and code. Clyx is an independent language-model project built around a custom decoder-only Transformer. The next planned BASE model targets approximately 635.51 million parameters, using Grouped-Query Attention, RoPE, RMSNorm, and SwiGLU. The intended training approach is random initialization and next-token prediction, without importing external pretrained weights. The goal is a Russian–English base model with exposure to programming languages, while keeping the architecture and training process documented. This is a BASE model project, not an instruction-tuned assistant. Any…

This model is a fine-tuned version of cosmos1030/gmp-kd3e-1-s80pct-lr1e-420260916220740 on the trl-lib/ultrafeedbackbinarized dataset. It has been trained using TRL. This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

DriveTwinLLM is an instruction-tuned, physics-grounded automotive diagnostic language model built on top of Qwen/Qwen2.5-3B-Instruct. It was fine-tuned using LoRA on NVIDIA B200 GPU hardware (180 GB VRAM) to achieve 98.13% evaluation token accuracy on real-world vehicle trouble codes, live ECU telemetry, and OEM service manuals. The model enforces strict zero-hallucination guardrails and features a specialized Dual-Persona Architecture: - Diagnostic computer scan fee ($90 – $130) - Certified shop labor ($120/hr flat-rate standard) - Strictly refuses unmonitored sensors (transmission fluid temperature, TPMS, cabin humidity) and invalid codes (P9999), redirecting the user to monitored engine…

Model · Text generation

dual-loop-qwen3.5-2b

Matthew

Official weights for the Dual-Loop Cognitive Controller on Qwen/Qwen3.5-2B ($D=2048$, Layer 11 hook, ~110M parameter deliberation adapter). The Dual-Loop Controller provides hardware-aligned, non-autoregressive System 2 deliberation directly within the latent residual stream of modern language models. It enables models to recursively deliberate in continuous hidden space without generating costly Chain-of-Thought (CoT) text tokens, eliminating KV-cache explosion and 30–60 second generation latencies. This adapter is strictly designed, calibrated, and hooked into the architectural dimensions of Qwen/Qwen3.5-2B: 1. Cognitive Matrix Helper (Tversky Elimination-by-Aspects): - Evaluates options…

Model · Text generation

DualMind_Methodolgy

Convergent Intelligence

Roy C Abstract. We present a four-stage pipeline for producing small language models (1.7B parameters) that exhibit self-critiquing dual-cognition reasoning from a 30B-parameter teacher. The pipeline chains: (1) proof-weighted knowledge distillation from three teacher variants (Instruct, Thinking, Coder) of Qwen3-30B-A3B, each producing distinct capability profiles in the student; (2) Topological Knowledge Distillation (TKD), which uses the Discrepancy Calculus (DISC) framework to decompose the teacher's output distribution into smooth, jump, and singular-continuous components via bounded variation theory, allocating training capacity to structural boundaries that standard KD smears across…

Model · Text generation

duogpt-40m-v1

Duoia Pung

从零训练的 38.85M 参数英文小模型，消费级显卡（RTX 4060 Laptop 8GB）单卡训完。 本仓库同时提供了 GGUF 格式（由 model/litmodel.pth 直接转换，模板保持与训练一致），可直接用 Ollama 导入： scripts/chat.sh 里的三种问法： 在 Python 里直接用： - 只会做被交代的任务：给它一句裸问题（如 What is a volcano?）它不会回答；必须像上面那样把任务写清楚。 - 上下文上限 512 token：prompt + 生成长度之和；超出会报 Cannot forward sequence of length T。 - 预训练语料：TinyStoriesV2-GPT4（CDLA-Sharing-1.0）与 Children-Stories（用户自备，含 GPT 生成内容） - 本包模型权重由上述数据从零训练得到，仅供研究与个人使用；若再分发请一并保留此说明与 MODELCARD.md。

This is a frozen snapshot of CL, EDOS Engineering's experimental continual-learning chatbot, deployed under the persona "Hades." CL learns while it runs by editing the model's original quantized weight tensors in place. Its training is self-directed: the model selects its own training corpus, decides what is important, and is then trained on what it selected. It reads new AI research papers in full, judges which findings matter, writes the training text itself as attributed reviews, and marks which of them to learn and how strongly. It also learns from conversation. It uses no LoRA, adapter or side network. This export is the live model after 1,091 online learning updates, merged into one…