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arcee-ai_afm-4.5b
AFM-4.5B is a 4.5 billion parameter instruction-tuned model developed by Arcee.ai, designed for enterprise-grade performance across diverse deployment environments from cloud to edge. The base model was trained on a dataset of 8 trillion tokens, comprising 6.5 trillion tokens of general pretraining data followed by 1.5 trillion tokens of midtraining data with enhanced focus on mathematical reasoning and code generation. Following pretraining, the model underwent supervised fine-tuning on high-quality instruction datasets. The instruction-tuned model was further refined through reinforcement learning on verifiable rewards as well as for human preference. We use a modified version of TorchTitan for pretraining, Axolotl for supervised fine-tuning, and a modified version of Verifiers for reinforcement learning.
The development of AFM-4.5B prioritized data quality as a fundamental requirement for achieving robust model performance. We collaborated with DatologyAI, a company specializing in large-scale data curation. DatologyAI's curation pipeline integrates a suite of proprietary algorithms—model-based quality filtering, embedding-based curation, target distribution-matching, source mixing, and synthetic data. Their expertise enabled the creation of a curated dataset tailored to support strong real-world performance.
The model architecture follows a standard transformer decoder-only design based on Vaswani et al., incorporating several key modifications for enhanced performance and efficiency. Notable architectural features include grouped query attention for improved inference efficiency and ReLU^2 activation functions instead of SwiGLU to enable sparsification while maintaining or exceeding performance benchmarks.
The model available in this repo is the instruct model following supervised fine-tuning and reinforcement learning.
Repository: localaiLicense: aml
jina-reranker-v1-tiny-en
This model is designed for blazing-fast reranking while maintaining competitive performance. What's more, it leverages the power of our JinaBERT model as its foundation. JinaBERT itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of ALiBi. This allows jina-reranker-v1-tiny-en to process significantly longer sequences of text compared to other reranking models, up to an impressive 8,192 tokens.
Repository: localai
eurollm-9b-instruct
The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages. EuroLLM-9B is a 9B parameter model trained on 4 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets. EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
Repository: localaiLicense: apache-2.0
phi-4
phi-4 is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.
phi-4 underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Phi-4 is a 14B parameters, dense decoder-only Transformer model.
Repository: localaiLicense: mit
LocalAI-functioncall-phi-4-v0.3
A model tailored to be conversational and execute function calls with LocalAI. This model is based on phi-4.
Repository: localaiLicense: mit
LocalAI-functioncall-phi-4-v0.2
A model tailored to be conversational and execute function calls with LocalAI. This model is based on phi-4.
This is the second iteration of https://huggingface.co/mudler/LocalAI-functioncall-phi-4-v0.1 with added CoT (o1) capabilities from the marco-o1 dataset.
Repository: localaiLicense: mit
LocalAI-functioncall-phi-4-v0.1
A model tailored to be conversational and execute function calls with LocalAI. This model is based on phi-4.
Repository: localaiLicense: mit
sicariussicariistuff_phi-lthy4
- The BEST Phi-4 Roleplay finetune in the world (Not that much of an achievement here, Phi roleplay finetunes can probably be counted on a single hand).
- Compact size & fully healed from the brain surgery Only 11.9B parameters. Phi-4 wasn't that hard to run even at 14B, now with even fewer brain cells, your new phone could probably run it easily. (SD8Gen3 and above recommended).
- Strong Roleplay & Creative writing abilities. This really surprised me. Actually good.
Writes and roleplays quite uniquely, probably because of lack of RP\writing slop in the pretrain. Who would have thought?
- Smart assistant with low refusals - It kept some of the smarts, and our little Phi-Lthy here will be quite eager to answer your naughty questions.
- Quite good at following the character card. Finally, it puts its math brain to some productive tasks. Gooner technology is becoming more popular by the day.
Repository: localaiLicense: mit
sicariussicariistuff_phi-line_14b
Excellent Roleplay with more brains. (Who would have thought Phi-4 models would be good at this? so weird... )
Medium length response (1-4 paragraphs, usually 2-3).
Excellent assistant that follows instructions well enough, and keeps good formating.
Strong Creative writing abilities. Will obey requests regarding formatting (markdown headlines for paragraphs, etc).
Writes and roleplays quite uniquely, probably because of lack of RP\writing slop in the pretrain. This is just my guesstimate.
LOW refusals - Total freedom in RP, can do things other RP models won't, and I'll leave it at that. Low refusals in assistant tasks as well.
VERY good at following the character card. Math brain is used for gooner tech, as it should be.
Repository: localaiLicense: mit
microsoft_phi-4-mini-instruct
Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures.
Repository: localaiLicense: mit
microsoft_phi-4-mini-reasoning
Phi-4-mini-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities. The model belongs to the Phi-4 model family and supports 128K token context length.
Phi-4-mini-reasoning is designed for multi-step, logic-intensive mathematical problem-solving tasks under memory/compute constrained environments and latency bound scenarios. Some of the use cases include formal proof generation, symbolic computation, advanced word problems, and a wide range of mathematical reasoning scenarios. These models excel at maintaining context across steps, applying structured logic, and delivering accurate, reliable solutions in domains that require deep analytical thinking.
This model is designed and tested for math reasoning only. It is not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models, as well as performance difference across languages, as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including but not limited to privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
This release of Phi-4-mini-reasoning addresses user feedback and market demand for a compact reasoning model. It is a compact transformer-based language model optimized for mathematical reasoning, built to deliver high-quality, step-by-step problem solving in environments where computing or latency is constrained. The model is fine-tuned with synthetic math data from a more capable model (much larger, smarter, more accurate, and better at following instructions), which has resulted in enhanced reasoning performance. Phi-4-mini-reasoning balances reasoning ability with efficiency, making it potentially suitable for educational applications, embedded tutoring, and lightweight deployment on edge or mobile systems. If a critical issue is identified with Phi-4-mini-reasoning, it should be promptly reported through the MSRC Researcher Portal or secure@microsoft.com
Repository: localaiLicense: mit
microsoft_phi-4-reasoning-plus
Phi-4-reasoning-plus is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. Phi-4-reasoning-plus has been trained additionally with Reinforcement Learning, hence, it has higher accuracy but generates on average 50% more tokens, thus having higher latency.
Repository: localaiLicense: mit
microsoft_phi-4-reasoning
Phi-4-reasoning is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.
Repository: localaiLicense: mit
phillama-3.8b-v0.1
The description of the LLM model is:
Phillama is a model based on Phi-3-mini and trained on Llama-generated dataset raincandy-u/Dextromethorphan-10k to make it more "llama-like". Also, this model is converted into Llama format, so it will work with any Llama-2/3 workflow. The model aims to generate text with a specific "llama-like" style and is suited for text-generation tasks.
Repository: localaiLicense: mit
scholasticlogicai-phi-4-i1
**Model Name:** ScholasticLogicAI-Phi-4
**Base Model:** Microsoft Phi-4
**Repository:** [Berthi-Rohar/ScholasticLogicAI-Phi-4](https://huggingface.co/Berthi-Rohar/ScholasticLogicAI-Phi-4)
**Description:**
A fine-tuned variant of Microsoft's Phi-4, specialized in logical reasoning using Aristotelian and scholastic logic frameworks. This model is designed to perform structured, deductive reasoning tasks, making it ideal for philosophical, analytical, and formal logic applications. It is part of an experimental project aimed at exploring the integration of classical logic paradigms into modern language models.
**Use Case:**
Best suited for tasks involving syllogistic reasoning, argument analysis, formal deduction, and logical consistency checks. Not intended for general conversation or casual use.
**Key Features:**
- Built on the Phi-4 architecture for high efficiency and performance.
- Fine-tuned for logical coherence and structured reasoning.
- Supports chat template input for interactive reasoning sessions.
**Note:** This model is experimental and may hallucinate or fail under complex or ambiguous prompts. Ideal for developers and researchers in logic, AI reasoning, and knowledge representation.
**License:** [MIT](https://github.com/Berthi-Rohar/ScholasticLogicAI-Phi-4/blob/main/LICENSE) (check original Phi-4 for base license).
*Quantized versions available via [mradermacher/ScholasticLogicAI-Phi-4-i1-GGUF](https://huggingface.co/mradermacher/ScholasticLogicAI-Phi-4-i1-GGUF) for local inference.*
Repository: localaiLicense: mit
logics-qwen3-math-4b
**Model Name:** Logics-Qwen3-Math-4B
**Base Model:** Qwen/Qwen3-4B-Thinking-2507
**Size:** 4B parameters
**Fine-Tuned For:** Mathematical reasoning, logical problem solving, and algorithmic coding
**Training Data:** OpenMathReasoning, OpenCodeReasoning, Helios-R-6M
**Description:**
A lightweight, high-precision 4B-parameter model optimized for mathematical and logical reasoning. Fine-tuned from Qwen3-4B-Thinking-2507, it excels in solving equations, performing step-by-step reasoning, and handling algorithmic tasks with structured outputs in LaTeX, Markdown, JSON, and more. Ideal for education, research, and deployment on mid-range hardware.
**Use Case:**
Perfect for math problem-solving, code reasoning, and technical content generation in resource-constrained environments.
**Tags:** #math #code #reasoning #4B #Qwen3 #text-generation #open-source
Repository: localaiLicense: apache-2.0
a2fm-32b-rl
**A²FM-32B-rl** is a 32-billion-parameter adaptive foundation model designed for hybrid reasoning and agentic tasks. It dynamically selects between *instant*, *reasoning*, and *agentic* execution modes using a **route-then-align** framework, enabling smarter, more efficient AI behavior.
Trained with **Adaptive Policy Optimization (APO)**, it achieves state-of-the-art performance on benchmarks like AIME25 (70.4%) and BrowseComp (13.4%), while reducing inference cost by up to **45%** compared to traditional reasoning methods—delivering high accuracy at low cost.
Originally developed by **PersonalAILab**, this model is optimized for tool-aware, multi-step problem solving and is ideal for advanced AI agents requiring both precision and efficiency.
🔹 *Model Type:* Adaptive Agent Foundation Model
🔹 *Size:* 32B
🔹 *Use Case:* Agentic reasoning, tool use, cost-efficient AI agents
🔹 *Training Approach:* Route-then-align + Adaptive Policy Optimization (APO)
🔹 *Performance:* SOTA on reasoning and agentic benchmarks
📄 [Paper](https://arxiv.org/abs/2510.12838) | 🐙 [GitHub](https://github.com/OPPO-PersonalAI/Adaptive_Agent_Foundation_Models)
Repository: localaiLicense: aml
gpt-oss-20b-esper3.1-i1
**Model Name:** gpt-oss-20b-Esper3.1
**Repository:** [ValiantLabs/gpt-oss-20b-Esper3.1](https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1)
**Base Model:** openai/gpt-oss-20b
**Type:** Instruction-tuned, reasoning-focused language model
**Size:** 20 billion parameters
**License:** Apache 2.0
---
### 🔍 **Overview**
gpt-oss-20b-Esper3.1 is a specialized, instruction-tuned variant of the 20B open-source GPT model, developed by **Valiant Labs**. It excels in **advanced coding, software architecture, and DevOps reasoning**, making it ideal for technical problem-solving and AI-driven engineering tasks.
### ✨ **Key Features**
- **Expert in DevOps & Cloud Systems:** Trained on high-difficulty datasets (e.g., Titanium3, Tachibana3, Mitakihara), it delivers precise, actionable guidance for AWS, Kubernetes, Terraform, Ansible, Docker, Jenkins, and more.
- **Strong Code Reasoning:** Optimized for complex programming tasks, including full-stack development, scripting, and debugging.
- **High-Quality Inference:** Uses `bf16` precision for full-precision performance; quantized versions (e.g., GGUF) available for efficient local inference.
- **Open-Source & Free to Use:** Fully open-access, built on the public gpt-oss-20b foundation and trained with community datasets.
### 📌 **Use Cases**
- Designing scalable cloud architectures
- Writing and optimizing infrastructure-as-code
- Debugging complex DevOps pipelines
- AI-assisted software development and documentation
- Real-time technical troubleshooting
### 💡 **Getting Started**
Use the standard `text-generation` pipeline with the `transformers` library. Supports role-based prompting (e.g., `user`, `assistant`) and performs best with high-reasoning prompts.
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="ValiantLabs/gpt-oss-20b-Esper3.1", torch_dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "Design a Kubernetes cluster for a high-traffic web app with CI/CD via GitHub Actions."}]
outputs = pipe(messages, max_new_tokens=2000)
print(outputs[0]["generated_text"][-1])
```
---
> 🔗 **Model Gallery Entry**:
> *gpt-oss-20b-Esper3.1 – A powerful, open-source 20B model tuned for expert-level DevOps, coding, and system architecture. Built by Valiant Labs using high-quality technical datasets. Perfect for engineers, architects, and AI developers.*
Repository: localaiLicense: apache-2.0