--- library_name: transformers license: apache-2.0 language: - en - zh base_model: openbmb/MiniCPM5-1B base_model_relation: finetune pipeline_tag: text-generation tags: - minicpm - minicpm5 - llama - text-generation - thinking - fable5 - coding - instruction-following ---

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

# MiniCPM5-1B-Claude-Opus-Fable5-Thinking > **πŸ“’ V2.0 is available** β€” 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](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking) > - GGUF: [MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF) GGUF quantizations for local deployment: **[MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF)** [δΈ­ζ–‡θ―΄ζ˜Ž](./README-cn.md) **MiniCPM5-1B-Claude-Opus-Fable5-Thinking** is a compact 1B **Thinking** language model built on [openbmb/MiniCPM5-1B](https://huggingface.co/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](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF)**. --- ## Overview | Item | Detail | |---|---| | **Base model** | [openbmb/MiniCPM5-1B](https://huggingface.co/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** β€” code generation, debugging, and software-engineering-style tasks - **Instruction following** β€” more reliable adherence to user prompts and structured constraints - **Thinking mode** β€” chain-of-thought reasoning via the MiniCPM5 chat template - **Tool calling** β€” inherits MiniCPM5's XML tool-call format - **Long context** β€” 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" 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](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` | --- ## Limitations - **Thinking outputs** β€” the model may emit reasoning blocks before the final answer; downstream apps can strip them before display - **1B scale** β€” optimized for lightweight local deployment, not frontier-scale general reasoning --- ## Provenance & licensing Released under **Apache-2.0**, inherited from [MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B). ## Acknowledgements - Base model: [OpenBMB / MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) - GGUF conversion: [llama.cpp](https://github.com/ggml-org/llama.cpp)