Instructions to use mayacinka/yam-pastiche-7B-ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayacinka/yam-pastiche-7B-ties with Transformers:

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

pipe = pipeline("text-generation", model="mayacinka/yam-pastiche-7B-ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayacinka/yam-pastiche-7B-ties")
model = AutoModelForCausalLM.from_pretrained("mayacinka/yam-pastiche-7B-ties")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mayacinka/yam-pastiche-7B-ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mayacinka/yam-pastiche-7B-ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/yam-pastiche-7B-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mayacinka/yam-pastiche-7B-ties

SGLang

How to use mayacinka/yam-pastiche-7B-ties 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 "mayacinka/yam-pastiche-7B-ties" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/yam-pastiche-7B-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mayacinka/yam-pastiche-7B-ties" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/yam-pastiche-7B-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mayacinka/yam-pastiche-7B-ties with Docker Model Runner:
```
docker model run hf.co/mayacinka/yam-pastiche-7B-ties
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

yam-pastiche-7B-ties

yam-pastiche-7B-ties is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: yam-peleg/Experiment28-7B
    parameters:
      weight: 0.25
      density: 0.8
  - model: yam-peleg/Experiment26-7B
    parameters:
      weight: 0.6
      density: 0.8
  - model: yam-peleg/Experiment24-7B
    parameters:
      weight: 0.15
      density: 0.8
base_model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
merge_method: ties
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/yam-pastiche-7B-ties"
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"])

Downloads last month: 1

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for mayacinka/yam-pastiche-7B-ties

yam-peleg/Experiment24-7B

yam-peleg/Experiment26-7B

yam-peleg/Experiment28-7B

Merge model

this model

Quantizations

1 model