Instructions to use schonsense/Cream_top2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schonsense/Cream_top2 with Transformers:

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

pipe = pipeline("text-generation", model="schonsense/Cream_top2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("schonsense/Cream_top2")
model = AutoModelForCausalLM.from_pretrained("schonsense/Cream_top2")
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

vLLM

How to use schonsense/Cream_top2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schonsense/Cream_top2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "schonsense/Cream_top2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/schonsense/Cream_top2

SGLang

How to use schonsense/Cream_top2 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 "schonsense/Cream_top2" \
    --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": "schonsense/Cream_top2",
		"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 "schonsense/Cream_top2" \
        --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": "schonsense/Cream_top2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use schonsense/Cream_top2 with Docker Model Runner:
```
docker model run hf.co/schonsense/Cream_top2
```

Cream_top2

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

Merge Details

Merge Method

This model was merged using the Model Breadcrumbs with TIES merge method using schonsense/ll3_3_70B_r128_VAR2 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: breadcrumbs_ties

models:
   
  - model: Delta-Vector/Austral-70B-Winton
    parameters:
      gamma: 0.01
      density: .2
      weight: 0.13
  - model: Delta-Vector/Shimamura-70B
    parameters:
      gamma: 0.01
      density: .2
      weight: 0.13
  - model: Darkhn/L3.3-70B-Animus-V7.0
    parameters:
      gamma: 0.01
      density: .5
      weight: 0.13
  - model: TheDrummer/Anubis-70B-v1.1
    parameters:
      gamma: 0.02
      density: .3
      weight: 0.13
  - model: schonsense/Llama3_3_70B_VAR_r128
    parameters:
      gamma: 0
      density: .7
      weight: 0.13
  - model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
    parameters:
      gamma: 0.01
      density: .3
      weight: 0.13
  - model: Tarek07/Scripturient-V1.3-LLaMa-70B
    parameters:
      gamma: 0.01
      density: .3
      weight: 0.13
  - model: zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B
    parameters:
      gamma: 0.02
      density: .2
      weight: 0.13
  - model: schonsense/ll3_3_70B_r128_VAR2

base_model: schonsense/ll3_3_70B_r128_VAR2
tokenizer_source: union
parameters:
  normalize: true
  int8_mask: true
  lambda: 0.95

dtype: float32
out_dtype: bfloat16

Downloads last month: 3

Model tree for schonsense/Cream_top2

Darkhn/L3.3-70B-Animus-V7.0

Delta-Vector/Austral-70B-Winton

Delta-Vector/Shimamura-70B

SentientAGI/Dobby-Unhinged-Llama-3.3-70B

Tarek07/Scripturient-V1.3-LLaMa-70B

TheDrummer/Anubis-70B-v1.1

zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B

Merge model

this model

Paper for schonsense/Cream_top2

Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks

Paper • 2312.06795 • Published Dec 11, 2023 • 2