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qwen3.6-35b-a3b-uncensored-genesis-hermes-v6
Qwen3.6-35B-A3B Uncensored Genesis Hermes V6 is LuffyTheFox's multimodal,
agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
combines Genesis tensor calibration with Hermes function-calling data while
retaining the 35B mixture-of-experts architecture, roughly 3B active
parameters per token, and the native 262K-token context window.
This entry installs the Q8_0 GGUF together with its F16 multimodal projector
for llama.cpp. The model card recommends Jinja chat templates and at least a
128K context for its thinking behavior. License: Apache-2.0.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7
Qwen3.6-35B-A3B Genesis Hermes V7 is LuffyTheFox's Apache-2.0 multimodal,
agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
combines Genesis tensor calibration with Hermes function-calling data while
retaining the 35B mixture-of-experts architecture, roughly 3B active
parameters per token, and the native 262K-token context window.
This entry's own payload uses the model card's recommended APEX GGUF and the
shared F16 multimodal projector. Automatic variant selection may instead
choose Compact APEX, an MTP-enabled APEX build, or Q8_K_P based on serving
features and available memory. The model card recommends Jinja chat
templates and at least a 128K context for its thinking behavior.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-final
Qwen3.6-35B-A3B Genesis Hermes Final is a multimodal mixture-of-experts
model with 35B total parameters and about 3B active per token. This
uncensored derivative combines the HauhauCS base with Hermes
function-calling data and the author's Genesis weight processing.
This build uses APEX and includes the F16 vision projector.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-final-apex-compact
Qwen3.6-35B-A3B Genesis Hermes Final is a multimodal mixture-of-experts
model with 35B total parameters and about 3B active per token. This
uncensored derivative combines the HauhauCS base with Hermes
function-calling data and the author's Genesis weight processing.
This build uses APEX Compact and includes the F16 vision projector.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-final-mtp-apex
Qwen3.6-35B-A3B Genesis Hermes Final is a multimodal mixture-of-experts
model with 35B total parameters and about 3B active per token. This
uncensored derivative combines the HauhauCS base with Hermes
function-calling data and the author's Genesis weight processing.
This build uses APEX and includes the F16 vision projector.
Native multi-token prediction is enabled for speculative decoding.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-final-mtp-apex-compact
Qwen3.6-35B-A3B Genesis Hermes Final is a multimodal mixture-of-experts
model with 35B total parameters and about 3B active per token. This
uncensored derivative combines the HauhauCS base with Hermes
function-calling data and the author's Genesis weight processing.
This build uses APEX Compact and includes the F16 vision projector.
Native multi-token prediction is enabled for speculative decoding.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-final-q8-k-p
Qwen3.6-35B-A3B Genesis Hermes Final is a multimodal mixture-of-experts
model with 35B total parameters and about 3B active per token. This
uncensored derivative combines the HauhauCS base with Hermes
function-calling data and the author's Genesis weight processing.
This build uses Q8_K_P and includes the F16 vision projector.
Repository: localaiLicense: apache-2.0
minicpm5-1b-claude-opus-fable5-v2-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. Compared with V1, this V2 release is further fine-tuned on **Fable 5** data with a stronger focus on **tool calling / function calling**, while also improving **coding** and **instruction-following**. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.
Previous version: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking** (V1)
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Tool calling (enhanced in V2)** — more reliable XML / function-calling style tool use on top of MiniCPM5's native format
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
...
Repository: localaiLicense: apache-2.0
lfm2.5-1.2b-nova-function-calling
The **LFM2.5-1.2B-Nova-Function-Calling-GGUF** is a quantized version of the original model, optimized for efficiency with **Unsloth**. It supports text and multimodal tasks, using different quantization levels (e.g., Q2_K, Q3_K, Q4_K, etc.) to balance performance and memory usage. The model is designed for function calling and is faster than the original version, making it suitable for tasks like code generation, reasoning, and multi-modal input processing.
Repository: localaiLicense: apache-2.0
granite-4.2-3b:vllm
Granite 4.2 3B is IBM's compact dense reasoning model for code generation,
tool calling, agentic workflows, multilingual chat, and long-context tasks.
This entry serves the bfloat16 safetensors with vLLM and supports a
128K-token context. It is the smallest fallback in a family that also offers
the higher-capacity 8B and 30B checkpoints as variants.
Repository: localaiLicense: apache-2.0
granite-4.2-8b:vllm
Granite 4.2 8B is IBM's mid-sized dense reasoning model for code generation,
tool calling, agentic workflows, multilingual chat, and long-context tasks.
This entry serves the higher-capacity bfloat16 safetensors with vLLM and
supports a 128K-token context.
Repository: localaiLicense: apache-2.0
granite-4.2-30b:vllm
Granite 4.2 30B is IBM's largest dense Granite 4.2 reasoning model for code
generation, tool calling, agentic workflows, multilingual chat, and
long-context tasks. This entry serves the bfloat16 safetensors with vLLM and
supports a 128K-token context.
Repository: localaiLicense: apache-2.0
ibm-granite_granite-4.0-h-small
Granite-4.0-H-Small is a 32B parameter long-context instruct model finetuned from Granite-4.0-H-Small-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.
Repository: localaiLicense: apache-2.0
ibm-granite_granite-4.0-h-tiny
Granite-4.0-H-Tiny is a 7B parameter long-context instruct model finetuned from Granite-4.0-H-Tiny-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.
Repository: localaiLicense: apache-2.0
ibm-granite_granite-4.0-micro
Granite-4.0-Micro is a 3B parameter long-context instruct model finetuned from Granite-4.0-Micro-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.
Repository: localaiLicense: apache-2.0
liquidai_lfm2-1.2b-tool
Based on LFM2-1.2B, LFM2-1.2B-Tool is designed for concise and precise tool calling. The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use.
Use cases:
Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency.
Real-time assistants in cars, IoT devices, or customer support, where response latency is critical.
Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution.
Repository: localaiLicense: lfm1.0
watt-ai_watt-tool-70b
watt-tool-70B is a fine-tuned language model based on LLaMa-3.3-70B-Instruct, optimized for tool usage and multi-turn dialogue. It achieves state-of-the-art performance on the Berkeley Function-Calling Leaderboard (BFCL).
Model Description
This model is specifically designed to excel at complex tool usage scenarios that require multi-turn interactions, making it ideal for empowering platforms like Lupan, an AI-powered workflow building tool. By leveraging a carefully curated and optimized dataset, watt-tool-70B demonstrates superior capabilities in understanding user requests, selecting appropriate tools, and effectively utilizing them across multiple turns of conversation.
Target Application: AI Workflow Building as in https://lupan.watt.chat/ and Coze.
Key Features
Enhanced Tool Usage: Fine-tuned for precise and efficient tool selection and execution.
Multi-Turn Dialogue: Optimized for maintaining context and effectively utilizing tools across multiple turns of conversation, enabling more complex task completion.
State-of-the-Art Performance: Achieves top performance on the BFCL, demonstrating its capabilities in function calling and tool usage.
Based on LLaMa-3.1-70B-Instruct: Inherits the strong language understanding and generation capabilities of the base model.
Repository: localaiLicense: apache-2.0
dolphin3.0-llama3.2-1b
Dolphin 3.0 is the next generation of the Dolphin series of instruct-tuned models. Designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.
Dolphin aims to be a general purpose model, similar to the models behind ChatGPT, Claude, Gemini. But these models present problems for businesses seeking to include AI in their products.
They maintain control of the system prompt, deprecating and changing things as they wish, often causing software to break.
They maintain control of the model versions, sometimes changing things silently, or deprecating older models that your business relies on.
They maintain control of the alignment, and in particular the alignment is one-size-fits all, not tailored to the application.
They can see all your queries and they can potentially use that data in ways you wouldn't want. Dolphin, in contrast, is steerable and gives control to the system owner. You set the system prompt. You decide the alignment. You have control of your data. Dolphin does not impose its ethics or guidelines on you. You are the one who decides the guidelines.
Dolphin belongs to YOU, it is your tool, an extension of your will. Just as you are personally responsible for what you do with a knife, gun, fire, car, or the internet, you are the creator and originator of any content you generate with Dolphin.
Repository: localaiLicense: llama3.2
dolphin3.0-llama3.2-3b
Dolphin 3.0 is the next generation of the Dolphin series of instruct-tuned models. Designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.
Dolphin aims to be a general purpose model, similar to the models behind ChatGPT, Claude, Gemini. But these models present problems for businesses seeking to include AI in their products.
They maintain control of the system prompt, deprecating and changing things as they wish, often causing software to break.
They maintain control of the model versions, sometimes changing things silently, or deprecating older models that your business relies on.
They maintain control of the alignment, and in particular the alignment is one-size-fits all, not tailored to the application.
They can see all your queries and they can potentially use that data in ways you wouldn't want. Dolphin, in contrast, is steerable and gives control to the system owner. You set the system prompt. You decide the alignment. You have control of your data. Dolphin does not impose its ethics or guidelines on you. You are the one who decides the guidelines.
Dolphin belongs to YOU, it is your tool, an extension of your will. Just as you are personally responsible for what you do with a knife, gun, fire, car, or the internet, you are the creator and originator of any content you generate with Dolphin.
Repository: localaiLicense: llama3.2
LocalAI-functioncall-llama3.2-1b-v0.4
A model tailored to be conversational and execute function calls with LocalAI. This model is based on llama 3.2 and has 1B parameter. Perfect for small devices.
Repository: localaiLicense: apache-2.0
LocalAI-functioncall-llama3.2-3b-v0.5
A model tailored to be conversational and execute function calls with LocalAI. This model is based on llama3.2 (3B).
Repository: localaiLicense: apache-2.0
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