Instructions to use cstr/llama3-8b-spaetzle-v33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/llama3-8b-spaetzle-v33 with Transformers:

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

pipe = pipeline("text-generation", model="cstr/llama3-8b-spaetzle-v33")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cstr/llama3-8b-spaetzle-v33")
model = AutoModelForCausalLM.from_pretrained("cstr/llama3-8b-spaetzle-v33")
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/llama3-8b-spaetzle-v33 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cstr/llama3-8b-spaetzle-v33"
# 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/llama3-8b-spaetzle-v33",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cstr/llama3-8b-spaetzle-v33

SGLang

How to use cstr/llama3-8b-spaetzle-v33 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/llama3-8b-spaetzle-v33" \
    --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/llama3-8b-spaetzle-v33",
		"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/llama3-8b-spaetzle-v33" \
        --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/llama3-8b-spaetzle-v33",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cstr/llama3-8b-spaetzle-v33 with Docker Model Runner:
```
docker model run hf.co/cstr/llama3-8b-spaetzle-v33
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama3-8b-spaetzle-v33

This is a merge of the following models:

It attempts a compromise in usefulness for German and English tasks.

For GGUF quants see cstr/llama3-8b-spaetzle-v33-GGUF,

Benchmarks

It achieves on EQ-Bench v2_de as q4km (old version without pre-tokenizer-fix) quants 66.59 (171 of 171 parseable) and 73.17 on v2 (english) (171/171).

For the int4-inc quants:

Benchmark	Score
Average	66.13
ARC-c	59.81
ARC-e	85.27
Boolq	84.10
HellaSwag	62.47
Lambada	73.28
MMLU	64.11
OpenbookQA	37.2
Piqa	80.30
TruthfulQA	50.21
Winogrande	73.72

Nous

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/Daredevil-8B 📄	55.87	44.13	73.52	59.05	46.77
cstr/llama3-8b-spaetzle-v33 📄	55.26	42.61	73.9	59.28	45.25
mlabonne/Daredevil-8B-abliterated 📄	55.06	43.29	73.33	57.47	46.17
NousResearch/Hermes-2-Theta-Llama-3-8B 📄	54.28	43.9	72.62	56.36	44.23
openchat/openchat-3.6-8b-20240522 📄	53.49	44.03	73.67	49.78	46.48
mlabonne/Llama-3-8B-Instruct-abliterated-dpomix 📄	52.26	41.6	69.95	54.22	43.26
meta-llama/Meta-Llama-3-8B-Instruct 📄	51.34	41.22	69.86	51.65	42.64
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 📄	51.21	40.23	69.5	52.44	42.69
mlabonne/OrpoLlama-3-8B 📄	48.63	34.17	70.59	52.39	37.36
meta-llama/Meta-Llama-3-8B 📄	45.42	31.1	69.95	43.91	36.7

🧩 Configuration

models:
  - model: cstr/llama3-8b-spaetzle-v20
    # no parameters necessary for base model
  - model: cstr/llama3-8b-spaetzle-v31
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3-8b-spaetzle-v28
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3-8b-spaetzle-v26
    parameters:
      density: 0.65
      weight: 0.15
merge_method: dare_ties
base_model: cstr/llama3-8b-spaetzle-v20
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/llama3-8b-spaetzle-v33"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])