Instructions to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking")
model = AutoModelForCausalLM.from_pretrained("GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking

SGLang

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking with Docker Model Runner:
```
docker model run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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