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spark-x2.5-1.7b-q4
Spark-X2.5-1.7B is XHToken's 1.7B text model for conversation, reasoning,
coding, and multilingual tasks. This build uses Q4_K_M GGUF weights,
the embedded Jinja chat template, and a 32K-token default context.
Repository: localaiLicense: apache-2.0
spark-x2.5-1.7b-q8
Spark-X2.5-1.7B is XHToken's 1.7B text model for conversation, reasoning,
coding, and multilingual tasks. This build uses Q8_0 GGUF weights,
the embedded Jinja chat template, and a 32K-token default context.
Repository: localaiLicense: apache-2.0
qwen3-tts-cpp-1.7b-base
Qwen3-TTS 1.7B Base (C++ / GGML, qwentts.cpp), Q8_0 (~2.0 GB talker).
Higher-quality streaming + voice cloning, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-base-q4
Qwen3-TTS 1.7B Base (C++ / GGML, qwentts.cpp), Q4_K_M (~1.2 GB talker).
Streaming + voice cloning, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-customvoice
Qwen3-TTS 1.7B CustomVoice (C++ / GGML, qwentts.cpp), Q8_0. Named speakers via
the `voice` field (serena, vivian, ryan, aiden, eric, dylan, ...). Streaming,
24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-customvoice-q4
Qwen3-TTS 1.7B CustomVoice (C++ / GGML, qwentts.cpp), Q4_K_M. Named speakers
via the `voice` field. Streaming, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-voicedesign
Qwen3-TTS 1.7B VoiceDesign (C++ / GGML, qwentts.cpp), Q8_0. Synthesises a
speaker from a free-text attribute instruction - REQUIRES the OpenAI
`instructions` field (e.g. "male, young adult, moderate pitch"); requests
without it are rejected. Streaming, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-voicedesign-q4
Qwen3-TTS 1.7B VoiceDesign (C++ / GGML, qwentts.cpp), Q4_K_M. Synthesises a
speaker from a free-text attribute instruction - REQUIRES the `instructions`
field. Streaming, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-1.7b-custom-voice
Qwen3-TTS is a high-quality text-to-speech model supporting custom voice, voice design, and voice cloning.
Repository: localaiLicense: apache-2.0
qwen3-asr-1.7b
Qwen3-ASR is an automatic speech recognition model supporting multiple languages and batch inference.
Repository: localaiLicense: apache-2.0
smolvlm-instruct
SmolVLM is a compact open multimodal model that accepts arbitrary sequences of image and text inputs to produce text outputs. Designed for efficiency, SmolVLM can answer questions about images, describe visual content, create stories grounded on multiple images, or function as a pure language model without visual inputs. Its lightweight architecture makes it suitable for on-device applications while maintaining strong performance on multimodal tasks.
Repository: localaiLicense: apache-2.0
qwen3-1.7b
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.
Qwen3-1.7B has the following features:
Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 1.7B
Number of Paramaters (Non-Embedding): 1.4B
Number of Layers: 28
Number of Attention Heads (GQA): 16 for Q and 8 for KV
Context Length: 32,768
Repository: localaiLicense: apache-2.0
menlo_lucy
Lucy is a compact but capable 1.7B model focused on agentic web search and lightweight browsing. Built on Qwen3-1.7B, Lucy inherits deep research capabilities from larger models while being optimized to run efficiently on mobile devices, even with CPU-only configurations.
We achieved this through machine-generated task vectors that optimize thinking processes, smooth reward functions across multiple categories, and pure reinforcement learning without any supervised fine-tuning.
Repository: localaiLicense: apache-2.0
menlo_lucy-128k
Lucy is a compact but capable 1.7B model focused on agentic web search and lightweight browsing. Built on Qwen3-1.7B, Lucy inherits deep research capabilities from larger models while being optimized to run efficiently on mobile devices, even with CPU-only configurations.
We achieved this through machine-generated task vectors that optimize thinking processes, smooth reward functions across multiple categories, and pure reinforcement learning without any supervised fine-tuning.
Repository: localaiLicense: apache-2.0
gustavecortal_beck-1.7b
A language model that handles delicate life situations and tries to really help you.
Beck is based on Piaget and was finetuned on psychotherapeutic preferences from PsychoCounsel-Preference.
Methodology
Beck was trained using preference optimization (ORPO) and LoRA. You can reproduce the results using my repo for lightweight preference optimization using this config that contains the hyperparameters.
This work was performed using HPC resources (Jean Zay supercomputer) from GENCI-IDRIS (Grant 20XX-AD011014205).
Inspiration
Beck aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.
Beck was inspired by my position paper on emotion analysis: Improving Language Models for Emotion Analysis: Insights from Cognitive Science.
Repository: localaiLicense: mit
smollm-1.7b-instruct
SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters.
These models are pre-trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost.
To build SmolLM-Instruct, we finetuned the base models on publicly available datasets.
Repository: localaiLicense: apache-2.0
smollm2-1.7b-instruct
SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.
The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.
Repository: localaiLicense: apache-2.0
qwen3-1.7b-crispasr
Qwen3-ASR 1.7B ASR. Runs via the CrispASR backend. Default GGUF size ~1.3 GB.
Repository: localai
lfm3-1.7b-uncensored
API
Try
Docs
Blog
# LFM3-1.7B
LFM3-1.7B is a **text model for local agents and specialized applications**, based on our new tri-hybrid architecture.
- **Agentic training:** post-trained for agentic harnesses such as Hermes Agent, Pi, and OpenClaw, with a 256K-token context window.
- **Designed for the edge:** at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop.
- **Made to be specialized:** adapt the model to your domains, formats, and tools via quick single-GPU fine-tuning.
Find more information about the LFM3 family in our release blog post.
## Model details
The LFM3 family includes base, text, vision, audio, omni, and decision models.
Property
Specification
Checkpoint
Post-trained, text-only language model
Parameters
1.73B
Architecture
24 layers: 13 gated short convolutions, 5 sliding-window attention, 6 global attention
Context length
256K tokens (input + output)
Vocabulary
128,000 tokens
Languages
English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
License
LFM Open License v1.0
...
Repository: localaiLicense: other