MiniCPM5-1B-Claude-Opus-Fable5-Thinking
📢 V2.0 is available — We have released an updated model with enhanced tool-calling capabilities. Welcome to try the new version:
GGUF quantizations for local deployment: MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF
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
Provenance & licensing
Released under Apache-2.0, inherited from MiniCPM5-1B.
Acknowledgements
- Base model: OpenBMB / MiniCPM5-1B
- GGUF conversion: llama.cpp