MiniCPM5 1B Claude Opus Fable5 Thinking

MiniCPM5 1B Claude Opus Fable5 Thinking by GnLOLot, a text-generation model. Understand and compare features, benchmarks, and capabilities.

Comparison

Feature	MiniCPM5 1B Claude Opus Fable5 Thinking	Interfaze
Input Modalities	text	image, text, audio, video, document
Native OCR	No	Yes
Long Document Processing	No	Yes
Language Support	unknown	162+
Native Speech-to-Text	No	Yes
Native Object Detection	No	Yes
Guardrail Controls	No	Yes
Context Input Size	131.1K	1M
Tool Calling	Yes	Tool calling supported + built in browser, code execution and web search

Scaling

Feature	MiniCPM5 1B Claude Opus Fable5 Thinking	Interfaze
Scaling	Self-hosted/Provider-hosted with quantization	Unlimited

View model card on Hugging Face

📢 V2.0 is available — We have released an updated model with enhanced tool-calling capabilities. Welcome to try the new version:

Transformers: MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking

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

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

Item	Detail
Base model	openbmb/MiniCPM5-1B (1B dense Llama architecture)
Post-training	Fable 5 traces
Key gains	Stronger coding and instruction following vs. the base checkpoint
Chat format	MiniCPM5 native Thinking template with optional chain-of-thought blocks
Context length	128K (`max_position_embeddings = 131072`)
Deployment	Single-GPU friendly; suitable for edge / local use

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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Sampling recommendations

Generation defaults are inherited from 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`

Limitations

Thinking outputs — the model may emit reasoning blocks before the final answer; downstream apps can strip them before display
1B scale — optimized for lightweight local deployment, not frontier-scale general reasoning