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: 全球首个 ikun 梗文化 AI 大模型 -- 完整学习路线分享

by kevinten10 - opened Mar 27

Discussion

kevinten10

IKUN org Mar 27

大家好！我是 ikun-2.5B 的开发者。

这是什么？

ikun-2.5B 是一个基于 MiniMind 微调的 26M 参数中文梗文化对话模型。名字里的 2.5B = 练习时长两年半（这本身就是梗）。

完整 LLM 学习路线

我们围绕 ikun 梗文化，开源了一套完整的 LLM 学习路线：

Level 0 ikun-basics — AI 基础知识
Level 1 ikun-tokenizer → ikun-pretrain → ikun-2.5B — 从分词器到 SFT
Level 2 ikun-DPO / ikun-GRPO / ikun-Reason — 对齐与推理
Level 3 ikun-MoE / ikun-Distill / ikun-V — 混合专家/蒸馏/多模态
Level 4 ikun-deploy — 部署上线

所有代码开源在 GitHub。

在线体验

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