Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
yam-peleg/Experiment28-7B
yam-peleg/Experiment26-7B
yam-peleg/Experiment24-7B
CorticalStack/pastiche-crown-clown-7b-dare-dpo
text-generation-inference
Instructions to use mayacinka/yam-pastiche-7B-franken with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayacinka/yam-pastiche-7B-franken with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mayacinka/yam-pastiche-7B-franken")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mayacinka/yam-pastiche-7B-franken") model = AutoModelForCausalLM.from_pretrained("mayacinka/yam-pastiche-7B-franken", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mayacinka/yam-pastiche-7B-franken 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-franken" # 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-franken", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mayacinka/yam-pastiche-7B-franken
- SGLang
How to use mayacinka/yam-pastiche-7B-franken 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-franken" \ --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-franken", "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-franken" \ --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-franken", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mayacinka/yam-pastiche-7B-franken with Docker Model Runner:
docker model run hf.co/mayacinka/yam-pastiche-7B-franken
yam-pastiche-7B-franken
yam-pastiche-7B-franken is a merge of the following models using LazyMergekit:
- yam-peleg/Experiment28-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment24-7B
- CorticalStack/pastiche-crown-clown-7b-dare-dpo
🧩 Configuration
slices:
- sources:
- model: yam-peleg/Experiment28-7B
layer_range: [0, 10]
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [10, 20]
- sources:
- model: yam-peleg/Experiment24-7B
layer_range: [20, 30]
- sources:
- model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
layer_range: [30, 32]
merge_method: passthrough
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayacinka/yam-pastiche-7B-franken"
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"])
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