---

license: apache-2.0

base_model: GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic

base_model_relation: quantized

language:

- en

- zh

pipeline_tag: text-generation

library_name: gguf

tags:

- gguf

- llama.cpp

- quantized

- minicpm5

- thinking

- fable5

- tool-calling

- function-calling

- agentic

- coding

- instruction-following

- conversational

---

<p align="center">

<img src="assets/banner.png" alt="MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-GGUF" width="100%"/>

</p>

MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-GGUF

GGUF quantizations of MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

This repository provides local-deployment builds of a 2B Thinking model fine-tuned on Claude data atop openbmb/MiniCPM5-2B, with a strong focus on agentic tool calling, coding, and instruction following. MiniCPM5's native chat template is embedded in the GGUF files.

Transformers checkpoint: MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic

---

Files

| File | Quant | Size | Notes |

|---|---|---|---|

| MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-F16.gguf | F16 | ~4.7 GB | full-precision conversion base |

| MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-Q8_0.gguf | Q8_0 | ~2.5 GB | recommended default |

| MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-Q4_K_M.gguf | Q4_K_M | ~1.5 GB | smallest, for memory-constrained devices |

Q8_0 is the recommended default quant for this 2B model. Q4_K_M is available for ultra-lightweight deployment.

---

Quick start

llama.cpp (llama-cli)

llama-cli \
  -m MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-Q8_0.gguf \
  -p "Write a Python function to merge two sorted lists." \
  -n 512 \
  --temp 1.0 --top-p 0.95 --min-p 0.0 \
  -c 8192

> The model supports up to 128K tokens (131,072) per config.json. Set -c according to your available VRAM/RAM.

llama.cpp server

llama-server \
  -m MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-Q8_0.gguf \
  -c 8192 --port 8080

LM Studio / jan / KoboldCpp

Load any .gguf file from this repository. The MiniCPM5 chat template is embedded in the GGUF metadata.

---

Sampling recommendations

Inherited from openbmb/MiniCPM5-2B:

| Scenario | Params |

|---|---|

| Default | temperature=1.0, top_p=0.95, min_p=0.0 |

| If repetitive outputs | temperature=1.0, top_p=0.95, min_p=0.0, repetition_penalty=1.05 |

This model is Thinking-only — chain-of-thought reasoning is always active.

> Support for sampling parameters varies across inference frameworks — check your runtime's documentation.

---

Capabilities

• Agentic tool calling — reliable function-calling / tool-use behavior designed for multi-step agentic workflows
• Claude fine-tune — post-trained on Claude data
• Coding — code generation, debugging, and software-engineering workflows
• Instruction following — reliable adherence to user prompts and task constraints
• Thinking mode — chain-of-thought reasoning; MiniCPM5 chat template baked into the GGUF
• Long context — up to 128K tokens (131,072 tokens per upstream config.json)

---

Benchmark

Scores for the Transformers checkpoint MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic:

ClawBench (Agentic Coding)

| Model | QwenClawBench | WildClawBench |

|---|---|---|

| MiniCPM5-2B | 42.11 | 23.19 |

| MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic | 44.56 (+2.45) | 24.32 (+1.13) |

> ClawBench evaluates agentic coding ability — the model's capacity to autonomously use tools, navigate codebases, and complete multi-step software engineering tasks.

> More benchmarks (BFCL, SWE-bench, Tau-Bench, etc.) coming soon.

---

Limitations

• Thinking outputs — the model may emit reasoning blocks before the final answer
• 2B scale — lightweight local deployment; not frontier-scale
• Runtime context — actual usable context depends on your GGUF runtime and hardware limits

---

Provenance & licensing

Apache-2.0, inherited from MiniCPM5-2B.

Acknowledgements

• Base model: OpenBMB / MiniCPM5-2B
• Transformers checkpoint: MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic
• Quantization: llama.cpp