Instructions to use aloobun/CosmicBun-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aloobun/CosmicBun-8B with Transformers:

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

pipe = pipeline("text-generation", model="aloobun/CosmicBun-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aloobun/CosmicBun-8B")
model = AutoModelForCausalLM.from_pretrained("aloobun/CosmicBun-8B")
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 aloobun/CosmicBun-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aloobun/CosmicBun-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aloobun/CosmicBun-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aloobun/CosmicBun-8B

SGLang

How to use aloobun/CosmicBun-8B 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 "aloobun/CosmicBun-8B" \
    --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": "aloobun/CosmicBun-8B",
		"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 "aloobun/CosmicBun-8B" \
        --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": "aloobun/CosmicBun-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

model

This is a merge of pre-trained language models created using mergekit.

Merge Method

This model was merged using the DARE TIES merge method using Locutusque/llama-3-neural-chat-v1-8b as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: Locutusque/llama-3-neural-chat-v1-8b
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
  normalize: 0.0
slices:
- sources:
  - layer_range: [0, 4]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.6
  - layer_range: [0, 4]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.6
      weight: 0.5
  - layer_range: [0, 4]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.5
- sources:
  - layer_range: [4, 8]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.8
      weight: 0.1
  - layer_range: [4, 8]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 1.0
      weight: 0.2
  - layer_range: [4, 8]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.7
- sources:
  - layer_range: [8, 12]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.7
      weight: 0.1
  - layer_range: [8, 12]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.7
      weight: 0.2
  - layer_range: [8, 12]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.7
      weight: 0.6
- sources:
  - layer_range: [12, 16]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.9
      weight: 0.2
  - layer_range: [12, 16]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.6
      weight: 0.6
  - layer_range: [12, 16]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.7
      weight: 0.3
- sources:
  - layer_range: [16, 20]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.2
  - layer_range: [16, 20]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 1.0
      weight: 0.2
  - layer_range: [16, 20]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.9
      weight: 0.4
- sources:
  - layer_range: [20, 24]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.7
      weight: 0.2
  - layer_range: [20, 24]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.9
      weight: 0.3
  - layer_range: [20, 24]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.4
- sources:
  - layer_range: [24, 28]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.4
  - layer_range: [24, 28]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.8
      weight: 0.2
  - layer_range: [24, 28]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 0.9
      weight: 0.4
- sources:
  - layer_range: [28, 32]
    model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 1.0
      weight: 0.3
  - layer_range: [28, 32]
    model: Weyaxi/Einstein-v6.1-Llama3-8B
    parameters:
      density: 0.9
      weight: 0.2
  - layer_range: [28, 32]
    model: Locutusque/llama-3-neural-chat-v1-8b
    parameters:
      density: 1.0
      weight: 0.3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.81
AI2 Reasoning Challenge (25-Shot)	61.86
HellaSwag (10-Shot)	84.29
MMLU (5-Shot)	65.53
TruthfulQA (0-shot)	54.08
Winogrande (5-shot)	78.85
GSM8k (5-shot)	68.23