Instructions to use liminerity/dhbacmes-3b-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liminerity/dhbacmes-3b-slerp with Transformers:

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

pipe = pipeline("text-generation", model="liminerity/dhbacmes-3b-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("liminerity/dhbacmes-3b-slerp")
model = AutoModelForCausalLM.from_pretrained("liminerity/dhbacmes-3b-slerp", 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 liminerity/dhbacmes-3b-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liminerity/dhbacmes-3b-slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liminerity/dhbacmes-3b-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liminerity/dhbacmes-3b-slerp

SGLang

How to use liminerity/dhbacmes-3b-slerp 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 "liminerity/dhbacmes-3b-slerp" \
    --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": "liminerity/dhbacmes-3b-slerp",
		"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 "liminerity/dhbacmes-3b-slerp" \
        --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": "liminerity/dhbacmes-3b-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use liminerity/dhbacmes-3b-slerp with Docker Model Runner:
```
docker model run hf.co/liminerity/dhbacmes-3b-slerp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

dhbacmes-3b-slerp

dhbacmes-3b-slerp is a merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
      - model: liminerity/herbaccbaccules-3b-slerp
        layer_range: [0, 40]
      - model: KnutJaegersberg/Deita-2b
        layer_range: [0, 40]
merge_method: slerp
base_model: liminerity/herbaccbaccules-3b-slerp
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	53.02
AI2 Reasoning Challenge (25-Shot)	45.22
HellaSwag (10-Shot)	70.77
MMLU (5-Shot)	52.94
TruthfulQA (0-shot)	40.41
Winogrande (5-shot)	65.11
GSM8k (5-shot)	43.67

Downloads last month: 78

Safetensors

Model size

3B params

Tensor type

F16

Model tree for liminerity/dhbacmes-3b-slerp

Quantizations

2 models

Evaluation results

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

45.220
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

70.770
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

52.940
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

40.410
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

65.110
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

43.670