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GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF

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Model Card

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF

GGUF quantizations of [MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking) for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

中文说明

This repository provides local-deployment builds of a 1B Thinking model fine-tuned on Fable 5 data (V2) atop openbmb/MiniCPM5-1B. Compared with V1, V2 strengthens tool calling / function calling, while keeping MiniCPM5's native chat template embedded in the GGUF files.

Transformers checkpoint: [MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking)

Previous GGUF version: [MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF) (V1)

Files

File	Quant	Size	Notes
`MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-Q8_0.gguf`	Q8_0	~1.1 GB	recommended default
`MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-F16.gguf`	F16	~2.1 GB	full-precision conversion base

Q8_0 is the recommended default quant for this 1B model.

Quick start

llama.cpp (`llama-cli`)

bash

llama-cli \
  -m MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-Q8_0.gguf \
  -p "Write a Python function to merge two sorted lists." \
  -n 512 \
  --temp 0.9 --top-p 0.95 \
  -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

bash

llama-server \
  -m MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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

Generation defaults are inherited from [MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B):

Mode	Params
Think (default)	`temperature=0.9, top_p=0.95`
No Think	`temperature=0.7, top_p=0.95`, `enable_thinking=False`

Capabilities

—Tool calling (enhanced in V2) — stronger function-calling / tool-use behavior
—Fable 5 fine-tune (V2) — post-trained on Fable 5 data
—Coding — code generation, debugging, and software-engineering workflows
—Instruction following — more 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 MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking:

BFCL + API-Bank

Model	BFCL non_live	BFCL live	API-Bank
MiniCPM5-1B (Base)	41.51%	60.24%	7.30%
MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking	43.06%	63.33%	22.10%

Tau-Bench

Domain	MiniCPM5-1B (Base)	MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
Airline	0.34 (17/50)	0.36 (18/50)
Retail	0.052 (6/115)	0.070 (8/115)

Limitations

—Thinking outputs — the model may emit reasoning blocks before the final answer
—1B 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 MiniCPM5-1B.

Acknowledgements

—Base model: OpenBMB / MiniCPM5-1B
—Transformers checkpoint: MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
—Quantization: llama.cpp