Instructions to use macadeliccc/SmaugDolphin-60B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macadeliccc/SmaugDolphin-60B with Transformers:

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

pipe = pipeline("text-generation", model="macadeliccc/SmaugDolphin-60B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/SmaugDolphin-60B")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/SmaugDolphin-60B")
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 macadeliccc/SmaugDolphin-60B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macadeliccc/SmaugDolphin-60B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/SmaugDolphin-60B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/macadeliccc/SmaugDolphin-60B

SGLang

How to use macadeliccc/SmaugDolphin-60B 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 "macadeliccc/SmaugDolphin-60B" \
    --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": "macadeliccc/SmaugDolphin-60B",
		"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 "macadeliccc/SmaugDolphin-60B" \
        --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": "macadeliccc/SmaugDolphin-60B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use macadeliccc/SmaugDolphin-60B with Docker Model Runner:
```
docker model run hf.co/macadeliccc/SmaugDolphin-60B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Smaug Dolphin 60B

This model is a MoErge of abacusai/Smaug-34B-v0.1 and cognitivecomputations/dolphin-2.2-yi-34b-200k

This model works as expected. Evaluations are running now.

GGUF + iMatrix

Available here

AWQ

TODO

Example output

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	76.44
AI2 Reasoning Challenge (25-Shot)	73.38
HellaSwag (10-Shot)	86.55
MMLU (5-Shot)	76.78
TruthfulQA (0-shot)	67.44
Winogrande (5-shot)	83.50
GSM8k (5-shot)	70.96

Downloads last month: 72

Safetensors

Model size

61B params

Tensor type

BF16

Model tree for macadeliccc/SmaugDolphin-60B

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

73.380
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.550
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

76.780
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

67.440
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.500
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

70.960