Instructions to use DRUPEs/CherryPit-1.1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DRUPEs/CherryPit-1.1-2B with Transformers:

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

pipe = pipeline("text-generation", model="DRUPEs/CherryPit-1.1-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DRUPEs/CherryPit-1.1-2B")
model = AutoModelForCausalLM.from_pretrained("DRUPEs/CherryPit-1.1-2B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DRUPEs/CherryPit-1.1-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DRUPEs/CherryPit-1.1-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DRUPEs/CherryPit-1.1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DRUPEs/CherryPit-1.1-2B

SGLang

How to use DRUPEs/CherryPit-1.1-2B 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 "DRUPEs/CherryPit-1.1-2B" \
    --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": "DRUPEs/CherryPit-1.1-2B",
		"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 "DRUPEs/CherryPit-1.1-2B" \
        --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": "DRUPEs/CherryPit-1.1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DRUPEs/CherryPit-1.1-2B with Docker Model Runner:
```
docker model run hf.co/DRUPEs/CherryPit-1.1-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CherryPit-1.1-2B

CherryPit-1.1-2B is the second release of CherryPit from DRUPEs (Dense Representation & Unified Parameter Encoding Systems), built on MiniCPM5-2B. It reasons much better inside a fixed thinking budget: with the same 8K thinking tokens it solves far more competition-math and coding problems than CherryPit-1.0, and it can now build React web apps from tests. Same size, architecture and speed as 1.0.

Results vs CherryPit-1.0

Both models were run on the same harness, items, prompts and seeds (bf16 weights in vLLM). "Thinking, 8K" means the model may think for up to 8,192 tokens before it answers; "–" = not measured for that model.

Benchmark	Setting	CherryPit-1.0	CherryPit-1.1	Δ
Math
AIME 2025	thinking, 8K, avg of 4	–	47.5	–
AIME 2026	thinking, 8K, avg of 4	–	56.7	–
HMMT Feb 2025	thinking, 8K, avg of 4	–	29.2	–
HMMT Feb 2026	thinking, 8K, avg of 4	–	27.3	–
AMC 2023	thinking, 8K, avg of 4	–	88.1	–
MATH-500	no thinking, greedy	78.2	87.8	+9.6
Coding
LiveCodeBench v6	thinking, 8K	29.1	46.3	+17.2
— hard problems	thinking, 8K, 80-problem probe	4.2	24.2	+20.0
LiveCodeBench v6	no thinking, avg of 4	31.7	31.0	−0.7
HumanEval+	no thinking, greedy	85.4	82.9	−2.5
MBPP+	no thinking, greedy	64.5	67.2	+2.7
Web development
WebApp1K-React	no thinking, 1,000 tasks	1.6	79.9	+78.3
FullStack Bench (web slice)	no thinking, 98 items x5	47.2	46.6	−0.6
Instruction following
IFEval	strict, prompt level	85.2	83.2	−2.0

Pooled over the 163 competition problems: 51.7. It also gets there with fewer tokens: on LiveCodeBench with thinking it uses 6,176 tokens per problem on average vs 7,454 for CherryPit-1.0.

Quantization

The Q4_K_M file was unpacked back to bf16 and re-run on the same harness, so this is what 4-bit costs. Thinking-mode coding is the most sensitive; if you have the memory, Q5_K_M or larger keeps more of it.

Benchmark	bf16	Q4_K_M	Δ
LiveCodeBench v6, thinking 8K	46.3	41.1	−5.2
LiveCodeBench v6, no thinking	31.0	29.6	−1.4
HumanEval+	82.9	84.8	+1.9
MBPP+	67.2	63.8	−3.4
MATH-500	87.8	84.8	−3.0
IFEval	83.2	81.9	−1.3

What's new in 1.1

Budget-aware reasoning: reinforcement learning under a fixed thinking budget, so the model reaches an answer inside 8K tokens instead of running out of room.
Ladder training on hard problems: the model starts from hints taken from its own long correct solutions, and the hints are removed step by step until it solves the problem on its own (math and code).
Knowing when to stop: once it has found the answer it commits to it instead of thinking on.
Web apps: React components written to pass a given set of tests (from the 1.1 WebDev preview).

Model Details

Developed by: DRUPEs
Model type: Causal language model
Parameters: ~2B
Base model: MiniCPM5-2B
Previous version: CherryPit-1.0-2B-GGUF
Architecture: Standard Llama
Context length: 128K tokens (131,072)
Tool calling: Supported (via the built-in chat template)
Language(s): English
License: Apache 2.0
Version: 1.1

How to Use

Full-precision (bf16) weights for transformers, vLLM and further fine-tuning. For llama.cpp, Ollama or LM Studio use DRUPEs/CherryPit-1.1-2B-GGUF.

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("DRUPEs/CherryPit-1.1-2B")
model = AutoModelForCausalLM.from_pretrained("DRUPEs/CherryPit-1.1-2B", torch_dtype="bfloat16", device_map="auto")
msgs = [{"role": "user", "content": "How many primes are there below 100?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=8192, do_sample=True, temperature=1.0, top_p=0.95)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

vllm serve DRUPEs/CherryPit-1.1-2B --max-model-len 32768

Recommended sampling: temperature 1.0, top_p 0.95. The template starts in thinking mode; pass enable_thinking=False to apply_chat_template (vLLM: chat_template_kwargs) for short, direct answers. With no system prompt the model introduces itself as CherryPit.

Limitations

It is a 2B model: it can be confidently wrong, so check answers that matter.
Instruction following (IFEval) and HumanEval+ are slightly below 1.0, see the table.
Tested in English only.

Downloads last month: 488

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for DRUPEs/CherryPit-1.1-2B

Base model

openbmb/MiniCPM5-2B

Finetuned

(82)

this model

Quantizations

1 model