Instructions to use antiven0m/gimlet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antiven0m/gimlet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="antiven0m/gimlet")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("antiven0m/gimlet") model = AutoModelForCausalLM.from_pretrained("antiven0m/gimlet") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use antiven0m/gimlet with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "antiven0m/gimlet" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "antiven0m/gimlet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/antiven0m/gimlet
- SGLang
How to use antiven0m/gimlet 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 "antiven0m/gimlet" \ --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": "antiven0m/gimlet", "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 "antiven0m/gimlet" \ --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": "antiven0m/gimlet", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use antiven0m/gimlet with Docker Model Runner:
docker model run hf.co/antiven0m/gimlet
Model Composition
NurtureAI/neural-chat-7b-v3-16k: Weight - 30%xDAN-AI/xDAN-L1-Chat-RL-v1: Weight - 30%rwitz/go-bruins-v2: Weight - 30%segmed/MedMistral-7B-v0.1: Weight - 10%
Code Snippet for Model Merging
The following Python code demonstrates how to create this mixed model using the LM-Cocktail approach:
from LM_Cocktail import mix_models_by_layers
model = mix_models_by_layers(
model_names_or_paths=[
"NurtureAI/neural-chat-7b-v3-16k",
"xDAN-AI/xDAN-L1-Chat-RL-v1",
"rwitz/go-bruins-v2",
"segmed/MedMistral-7B-v0.1"
],
model_type='decoder',
weights=[0.3, 0.3, 0.3, 0.1],
output_path='./mixed_llm'
)
license: apache-2.0
- Downloads last month
- 7