Instructions to use cstr/Spaetzle-v8-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/Spaetzle-v8-7b with Transformers:

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

pipe = pipeline("text-generation", model="cstr/Spaetzle-v8-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/Spaetzle-v8-7b")
model = AutoModelForCausalLM.from_pretrained("cstr/Spaetzle-v8-7b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cstr/Spaetzle-v8-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/Spaetzle-v8-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cstr/Spaetzle-v8-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/Spaetzle-v8-7b

SGLang

How to use cstr/Spaetzle-v8-7b 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 "cstr/Spaetzle-v8-7b" \
    --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": "cstr/Spaetzle-v8-7b",
		"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 "cstr/Spaetzle-v8-7b" \
        --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": "cstr/Spaetzle-v8-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/Spaetzle-v8-7b with Docker Model Runner:
```
docker model run hf.co/cstr/Spaetzle-v8-7b
```

cstr commited on Apr 18, 2024

Commit

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@@ -48,6 +48,26 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 EQ-Bench (v2_de): 61.04 / english (v2): 78.3
 |                           Model                            |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
 |------------------------------------------------------------|------:|------:|---------:|-------:|------:|
 |[Spaetzle-v8-7b](https://huggingface.co/cstr/Spaetzle-v8-7b)|  45.31|  75.69|     63.94|   45.57|  57.63|

 EQ-Bench (v2_de): 61.04 / english (v2): 78.3
+[ScandEval](https://scandeval.com/german-nlg/) 12.5.2 scores
+| Benchmark             | Spaetzle-v8-7b Value                               |
+|-----------------------|----------------------------------------------------|
+| Model ID              | cstr/Spaetzle-v8-7b (few-shot, val)                |
+| Parameters            | 7242                                               |
+| Vocabulary Size       | 32                                                 |
+| Context               | 32768                                              |
+| Commercial            | False                                              |
+| Speed                 | 5,980 ± 1,031 / 1,714 ± 552                        |
+| Rank                  | 1.85                                               |
+| GermEval              | 58.90 ± 2.30 / 45.55 ± 3.30                        |
+| SB10k                 | 61.34 ± 1.90 / 72.98 ± 1.30                        |
+| ScaLA-De              | 31.58 ± 4.39 / 65.51 ± 2.23                        |
+| GermanQuAD            | 24.91 ± 3.98 / 60.88 ± 3.31                        |
+| MLSum                 | 67.25 ± 1.06 / 22.95 ± 2.64                        |
+| MMLU-De               | 34.62 ± 2.20 / 50.43 ± 1.52                        |
+| HellaSwag-De          | 48.70 ± 2.47 / 61.05 ± 1.79                        |
 |                           Model                            |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
 |------------------------------------------------------------|------:|------:|---------:|-------:|------:|
 |[Spaetzle-v8-7b](https://huggingface.co/cstr/Spaetzle-v8-7b)|  45.31|  75.69|     63.94|   45.57|  57.63|