GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking · Hugging Face

Source: https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#minicpm5-1b-claude-opus-fable5-thinkingMiniCPM5-1B-Claude-Opus-Fable5-Thinking

📢 V2.0 is available— We have released an updated model withenhanced tool-callingcapabilities. 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-Thinkingis a compact 1BThinkinglanguage model built onopenbmb/MiniCPM5-1B. It is further fine-tuned onFable 5data to improvecodingandinstruction-followingwhile keeping MiniCPM5’s native Thinking chat template and tool-call format.

For llama.cpp / Ollama / LM Studio deployment, see the**GGUF repository**.

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#overviewOverview

ItemDetailBase modelopenbmb/MiniCPM5-1B(1B dense Llama architecture)Post-trainingFable 5 tracesKey gainsStronger coding and instruction following vs. the base checkpointChat formatMiniCPM5 native Thinking template with optional chain-of-thought blocksContext length****128K(max\_position\_embeddings = 131072)DeploymentSingle-GPU friendly; suitable for edge / local use

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#capabilitiesCapabilities

• 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 to128K tokens(131,072 tokens perconfig\.json)

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#quick-startQuick 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))

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#sampling-recommendationsSampling recommendations

Generation defaults are inherited from**MiniCPM5-1B**:

ModeParamsThink(default)temperature=0\.9, top\_p=0\.95No Thinktemperature=0\.7, top\_p=0\.95,enable\_thinking=False

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#limitationsLimitations

• 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

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#provenance–licensingProvenance & licensing

Released underApache-2.0, inherited fromMiniCPM5-1B.

https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking#acknowledgementsAcknowledgements

• Base model:OpenBMB / MiniCPM5-1B
• GGUF conversion:llama.cpp