Text Generation
Transformers
Safetensors
llama
Merge
mergekit
lazymergekit
lightblue/karasu-1.1B
TinyLlama/TinyLlama-1.1B-Chat-v1.0
text-generation-inference
Instructions to use OT20230122/karasu-base-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OT20230122/karasu-base-slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OT20230122/karasu-base-slerp")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OT20230122/karasu-base-slerp") model = AutoModelForCausalLM.from_pretrained("OT20230122/karasu-base-slerp") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OT20230122/karasu-base-slerp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OT20230122/karasu-base-slerp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OT20230122/karasu-base-slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OT20230122/karasu-base-slerp
- SGLang
How to use OT20230122/karasu-base-slerp 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 "OT20230122/karasu-base-slerp" \ --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": "OT20230122/karasu-base-slerp", "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 "OT20230122/karasu-base-slerp" \ --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": "OT20230122/karasu-base-slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OT20230122/karasu-base-slerp with Docker Model Runner:
docker model run hf.co/OT20230122/karasu-base-slerp
karasu-base-slerp
karasu-base-slerp is a merge of the following models using LazyMergekit:
๐งฉ Configuration
slices:
- sources:
- model: lightblue/karasu-1.1B
layer_range: [0, 16]
- model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
layer_range: [6, 22]
merge_method: slerp
base_model: lightblue/karasu-1.1B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
๐ป Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "OT20230122/karasu-base-slerp"
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
- 3
Model tree for OT20230122/karasu-base-slerp
Merge model
this model