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nvcky
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openbmb/MiniCPM5-1B
Input Modalities
Output Modalities
Supported Functionality
| 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 |
config.json)python
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))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 |
Released under Apache-2.0, inherited from MiniCPM5-1B.
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))