Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE
Sao10K/Fimbulvetr-11B-v2
text-generation-inference
Instructions to use grenforcer/experiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grenforcer/experiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="grenforcer/experiment")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("grenforcer/experiment") model = AutoModelForCausalLM.from_pretrained("grenforcer/experiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use grenforcer/experiment with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "grenforcer/experiment" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "grenforcer/experiment", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/grenforcer/experiment
- SGLang
How to use grenforcer/experiment 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 "grenforcer/experiment" \ --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": "grenforcer/experiment", "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 "grenforcer/experiment" \ --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": "grenforcer/experiment", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use grenforcer/experiment with Docker Model Runner:
docker model run hf.co/grenforcer/experiment
| tags: | |
| - merge | |
| - mergekit | |
| - lazymergekit | |
| - SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE | |
| - Sao10K/Fimbulvetr-11B-v2 | |
| base_model: | |
| - SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE | |
| - Sao10K/Fimbulvetr-11B-v2 | |
| # FrankenMaid | |
| FrankenMaid is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): | |
| * [SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE](https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE) | |
| * [Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2) | |
| ## 🧩 Configuration | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE | |
| layer_range: [0, 32] | |
| - sources: | |
| - model: Sao10K/Fimbulvetr-11B-v2 | |
| layer_range: [32, 48] | |
| merge_method: passthrough | |
| dtype: bfloat16 | |
| ``` | |
| ## 💻 Usage | |
| ```python | |
| !pip install -qU transformers accelerate | |
| from transformers import AutoTokenizer | |
| import transformers | |
| import torch | |
| model = "grenforcer/FrankenMaid" | |
| 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"]) | |
| ``` |