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atomic-minicpm-v-4.7-35b-a3b-uncensored-q4
AtomicChat's abliterated MiniCPM-V 4.7 is a 35B mixture-of-experts model
with 3B active parameters for chat and image understanding. This
AD-Q4_K-IQ4_XS build includes all 11 weight shards and the F16 vision
projector, with the embedded chat template and a 32K-token default context.
Repository: localaiLicense: apache-2.0
atomic-minicpm-v-4.7-35b-a3b-uncensored-q6
AtomicChat's abliterated MiniCPM-V 4.7 is a 35B mixture-of-experts model
with 3B active parameters for chat and image understanding. This
AD-Q6_K build includes all 16 weight shards and the F16 vision
projector, with the embedded chat template and a 32K-token default context.
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
minicpm5-1b-claude-opus-fable5-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. It is further fine-tuned on **Fable 5** data to improve **coding** and **instruction-following** while keeping MiniCPM5's native Thinking chat template and tool-call format.
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **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
- **Tool calling** — inherits MiniCPM5's XML tool-call format
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"
...
Repository: localaiLicense: apache-2.0
minicpm-ocr-1.0-q8
MiniCPM-OCR 1.0 is OpenBMB's 1.3B document-parsing model for Chinese and
English text, tables, formulas, charts, and seals. This entry uses the
official Q8_0 GGUF weights and the required F16 4x vision projector
through llama.cpp, with an 8,192-token default context. Prompt the model
with "Text Recognition:" for text, "Table Recognition:" for tables, or
"Formula Recognition:" for formulas. Table output is the model's OTSL
markup, which clients can convert to HTML.
Repository: localaiLicense: apache-2.0
minicpm-ocr-1.0-q4km
MiniCPM-OCR 1.0 in the smaller Q4_K_M GGUF build, with the required F16
4x vision projector and an 8,192-token default context. Tables stay
lossless at this size; dense text may degrade slightly. Prompt the model
with "Text Recognition:" for text, "Table Recognition:" for tables, or
"Formula Recognition:" for formulas. Table output is the model's OTSL
markup, which clients can convert to HTML.
Repository: localaiLicense: apache-2.0
minicpm-ocr-1.0-dspark
MiniCPM-OCR 1.0 with OpenBMB's DSpark speculative drafter. DSpark
proposes blocks of tokens that the target verifies, which speeds up
generation without changing the target model's output. This entry pairs
the Q8_0 target with the matching Q8_0 draft and the required F16 4x
vision projector, with an 8,192-token default context.
Repository: localaiLicense: apache-2.0
minicpm-v-4_5
MiniCPM-V 4.5 is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters.
Repository: localaiLicense: apache-2.0
minicpm-v-4_6
MiniCPM-V 4.6 is the most edge-deployment-friendly model in the MiniCPM-V series, with a total of 1.3B parameters. Built on Qwen3.5-0.8B and SigLIP2-400M, it features ultra-efficient architecture with mixed 4x/16x visual token compression for on-device deployment on iOS, Android, and HarmonyOS.
Repository: localaiLicense: apache-2.0
minicpm-v-4_7-1b-q4
MiniCPM-V 4.7 1B is OpenBMB's compact vision-language model for
text chat and image understanding. This entry uses the official
Q4_K_M GGUF weights and F16 vision projector through llama.cpp,
with an 8,192-token default context.
Repository: localaiLicense: apache-2.0
minicpm-v-4_7-1b-q5
MiniCPM-V 4.7 1B is OpenBMB's compact vision-language model for
text chat and image understanding. This entry uses the official
Q5_K_M GGUF weights and F16 vision projector through llama.cpp,
with an 8,192-token default context.
Repository: localaiLicense: apache-2.0
minicpm-v-4_7-1b-q6
MiniCPM-V 4.7 1B is OpenBMB's compact vision-language model for
text chat and image understanding. This entry uses the official
Q6_K GGUF weights and F16 vision projector through llama.cpp,
with an 8,192-token default context.
Repository: localaiLicense: apache-2.0
minicpm-v-4_7-1b-q8
MiniCPM-V 4.7 1B is OpenBMB's compact vision-language model for
text chat and image understanding. This entry uses the official
Q8_0 GGUF weights and F16 vision projector through llama.cpp,
with an 8,192-token default context.
Repository: localaiLicense: apache-2.0
minicpm5-2b
MiniCPM5-2B is OpenBMB's compact Llama-based language model for English
and Chinese chat, coding, and reasoning. This entry uses the official
Q4_K_M GGUF quantization and the model's embedded chat template.
Repository: localaiLicense: apache-2.0
minicpm5-2b-q8
MiniCPM5-2B is OpenBMB's compact Llama-based language model for English
and Chinese chat, coding, and reasoning. This entry uses the official
Q8_0 GGUF quantization and the model's embedded chat template.
Repository: localaiLicense: apache-2.0
minicpm5-2b-f16
MiniCPM5-2B is OpenBMB's compact Llama-based language model for English
and Chinese chat, coding, and reasoning. This entry uses the official
F16 GGUF build and the model's embedded chat template.
Repository: localaiLicense: apache-2.0
minicpm5-1b
MiniCPM5-1B is a compact 1B-parameter language model based on the LLaMA architecture. Despite its small size, it achieves competitive performance among sub-2B models, making it ideal for edge deployment and resource-constrained environments.
Repository: localaiLicense: apache-2.0
minicpm5-1b-q8
MiniCPM5-1B is a compact 1B-parameter language model based on the LLaMA architecture. This entry uses the higher-quality Q8_0 GGUF quantization for edge deployment and resource-constrained environments.
Repository: localaiLicense: apache-2.0
minicpm-o-4_5
MiniCPM-o 4.5 is the latest omni-modal model in the MiniCPM series. Built on Qwen3-8B and SigLIP2-400M, it supports vision, speech, and text in a unified 8B-parameter architecture with full-duplex real-time streaming.
Repository: localaiLicense: apache-2.0
minicpm-v-4_6-thinking
MiniCPM-V 4.6 Thinking is the reasoning-enhanced version of MiniCPM-V 4.6, with a total of 1.3B parameters. Built on Qwen3.5-0.8B and SigLIP2-400M, it features extended thinking capabilities for complex visual reasoning tasks while maintaining an ultra-compact architecture for edge deployment.
Repository: localaiLicense: apache-2.0
minicpm-v-4
MiniCPM-V 4 is a multimodal large language model in the MiniCPM-V series, supporting image and video understanding with strong OCR and multi-image capabilities.
Repository: localaiLicense: apache-2.0
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