Instructions to use tklohj/WindyFloLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tklohj/WindyFloLLM with Transformers:

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

pipe = pipeline("text-generation", model="tklohj/WindyFloLLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tklohj/WindyFloLLM")
model = AutoModelForMultimodalLM.from_pretrained("tklohj/WindyFloLLM")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tklohj/WindyFloLLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tklohj/WindyFloLLM

SGLang

How to use tklohj/WindyFloLLM 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 "tklohj/WindyFloLLM" \
    --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": "tklohj/WindyFloLLM",
		"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 "tklohj/WindyFloLLM" \
        --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": "tklohj/WindyFloLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tklohj/WindyFloLLM with Docker Model Runner:
```
docker model run hf.co/tklohj/WindyFloLLM
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: psmathur/orca_mini_v3_13b
dtype: float16
merge_method: slerp
parameters:
  t:
  - filter: self_attn
    value: [0.0, 0.5, 0.3, 0.7, 1.0]
  - filter: mlp
    value: [1.0, 0.5, 0.7, 0.3, 0.0]
  - value: 0.5
slices:
- sources:
  - layer_range: [0, 40]
    model: psmathur/orca_mini_v3_13b
  - layer_range: [0, 40]
    model: garage-bAInd/Platypus2-13B

Downloads last month: 7

Safetensors

Model size

13B params

Tensor type

F16

Model tree for tklohj/WindyFloLLM

garage-bAInd/Platypus2-13B

pankajmathur/orca_mini_v3_13b

Merge model

this model

Quantizations

2 models