Instructions to use IKUN-LLM/ikun-2.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IKUN-LLM/ikun-2.5B with Transformers:

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

pipe = pipeline("text-generation", model="IKUN-LLM/ikun-2.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IKUN-LLM/ikun-2.5B")
model = AutoModelForCausalLM.from_pretrained("IKUN-LLM/ikun-2.5B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IKUN-LLM/ikun-2.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IKUN-LLM/ikun-2.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IKUN-LLM/ikun-2.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IKUN-LLM/ikun-2.5B

SGLang

How to use IKUN-LLM/ikun-2.5B 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 "IKUN-LLM/ikun-2.5B" \
    --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": "IKUN-LLM/ikun-2.5B",
		"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 "IKUN-LLM/ikun-2.5B" \
        --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": "IKUN-LLM/ikun-2.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IKUN-LLM/ikun-2.5B with Docker Model Runner:
```
docker model run hf.co/IKUN-LLM/ikun-2.5B
```

ikun-2.5B roadmap: what should the next improvement focus on?

by kevinten10 - opened 17 days ago

Discussion

kevinten10

IKUN org 17 days ago

ikun-2.5B roadmap: what should the next improvement focus on?

We are planning the next iteration of ikun-2.5B and would like concrete feedback from people who have tried the model.

The current public model card describes the project as a 25.83M-parameter Chinese dialogue model based on MiniMind2-Small, with 214 SFT conversations, a 6,400-token BPE vocabulary, and a maximum context length of 32,768 tokens. The model is intended for entertainment and educational experimentation rather than serious production use.

The documented limitations are also clear: the small parameter count limits generation quality, longer outputs can become repetitive or grammatically inconsistent, and the model is not suitable for high-stakes scenarios.

For the next iteration, which area would be most useful?

Expand and clean the dialogue dataset.
Add a reproducible evaluation set and baseline results.
Improve inference examples for Transformers, llama.cpp, vLLM, and Ollama.
Improve the hosted demo and collect structured failure cases.

Please share a concrete prompt, failure example, or evaluation idea when possible. That will help turn feedback into a testable improvement rather than a vague feature request.

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