Instructions to use whcl412/FinchAI-Rune2-Luna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use whcl412/FinchAI-Rune2-Luna with Transformers:

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

pipe = pipeline("text-generation", model="whcl412/FinchAI-Rune2-Luna", trust_remote_code=True)
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("whcl412/FinchAI-Rune2-Luna", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("whcl412/FinchAI-Rune2-Luna", trust_remote_code=True, 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 whcl412/FinchAI-Rune2-Luna with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "whcl412/FinchAI-Rune2-Luna"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "whcl412/FinchAI-Rune2-Luna",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/whcl412/FinchAI-Rune2-Luna

SGLang

How to use whcl412/FinchAI-Rune2-Luna 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 "whcl412/FinchAI-Rune2-Luna" \
    --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": "whcl412/FinchAI-Rune2-Luna",
		"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 "whcl412/FinchAI-Rune2-Luna" \
        --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": "whcl412/FinchAI-Rune2-Luna",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use whcl412/FinchAI-Rune2-Luna with Docker Model Runner:
```
docker model run hf.co/whcl412/FinchAI-Rune2-Luna
```

whcl412 commited on 2 days ago

Commit

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@@ -24,6 +24,8 @@ tags:
 > 相比 v2（[`whcl412/FinchAI-Rune2-f16`](https://huggingface.co/whcl412/FinchAI-Rune2-f16)）：
 > 补了 139 条多轮样本，**多轮复读从 1 次降到 0 次**，其余能力全部保持。
 ![RinchAI 三代能力对比](./benchmark.png)
 > 图中数据：Rune1 为 7 项探针 × 5 次抽样，Rune2 为 14 项探针 × 2 次抽样，
@@ -31,7 +33,8 @@ tags:
 | | |
 |---|---|
-| **本仓库** | `RinchAI-Rune2-Luna`（bfloat16 完整权重，3.24 GiB） |
 | **能力范围** | 纯文本（**不含视觉**） |
 | **上下文窗口** | 262144（256K），与底座一致；**但只在 1024 token 内训练过** |
 | **加载方式** | `AutoModelForCausalLM` + `trust_remote_code=True` |

 > 相比 v2（[`whcl412/FinchAI-Rune2-f16`](https://huggingface.co/whcl412/FinchAI-Rune2-f16)）：
 > 补了 139 条多轮样本，**多轮复读从 1 次降到 0 次**，其余能力全部保持。
+**精度：f16（bfloat16 完整权重，3.24 GiB，未量化）**。仓库名不带精度后缀，以此处为准。
 ![RinchAI 三代能力对比](./benchmark.png)
 > 图中数据：Rune1 为 7 项探针 × 5 次抽样，Rune2 为 14 项探针 × 2 次抽样，
 | | |
 |---|---|
+| **本仓库** | `RinchAI-Rune2-Luna` |
+| **精度** | **f16 / bfloat16 完整权重**（3.24 GiB，未量化、未蒸馏） |
 | **能力范围** | 纯文本（**不含视觉**） |
 | **上下文窗口** | 262144（256K），与底座一致；**但只在 1024 token 内训练过** |
 | **加载方式** | `AutoModelForCausalLM` + `trust_remote_code=True` |