Instructions to use Kquant03/EarthRender-32x7B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/EarthRender-32x7B-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="Kquant03/EarthRender-32x7B-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kquant03/EarthRender-32x7B-bf16")
model = AutoModelForCausalLM.from_pretrained("Kquant03/EarthRender-32x7B-bf16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kquant03/EarthRender-32x7B-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kquant03/EarthRender-32x7B-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/EarthRender-32x7B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kquant03/EarthRender-32x7B-bf16

SGLang

How to use Kquant03/EarthRender-32x7B-bf16 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 "Kquant03/EarthRender-32x7B-bf16" \
    --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": "Kquant03/EarthRender-32x7B-bf16",
		"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 "Kquant03/EarthRender-32x7B-bf16" \
        --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": "Kquant03/EarthRender-32x7B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kquant03/EarthRender-32x7B-bf16 with Docker Model Runner:
```
docker model run hf.co/Kquant03/EarthRender-32x7B-bf16
```

Kquant03 commited on Jan 4, 2024

Commit

971b309

1 Parent(s): 79d4822

Update README.md

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@@ -8,7 +8,7 @@ language:
 # I am become death, destroyer of worlds.
-...32 experts in one model...at glorious 7B. Uses AIDC-ai-business/Marcoroni-7B-v3, Toten5/Marcoroni-neural-chat-7B-v2, HuggingFaceH4/zephyr-7b-beta, NurtureAI/neural-chat-7b-v3-16k, mlabonne/NeuralPipe-7B-ties, mlabonne/NeuralHermes-2.5-Mistral-7B, cognitivecomputations/dolphin-2.6-mistral-7b-dpo, SanjiWatsuki/Silicon-Maid-7B and xDAN-AI/xDAN-L1-Chat-RL-v1.
 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)"
 ### (from the MistralAI papers...click the quoted question above to navigate to it directly.)

 # I am become death, destroyer of worlds.
+...32 experts in one frankenMoE...at glorious 7B. Uses AIDC-ai-business/Marcoroni-7B-v3, Toten5/Marcoroni-neural-chat-7B-v2, HuggingFaceH4/zephyr-7b-beta, NurtureAI/neural-chat-7b-v3-16k, mlabonne/NeuralPipe-7B-ties, mlabonne/NeuralHermes-2.5-Mistral-7B, cognitivecomputations/dolphin-2.6-mistral-7b-dpo, SanjiWatsuki/Silicon-Maid-7B and xDAN-AI/xDAN-L1-Chat-RL-v1.
 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)"
 ### (from the MistralAI papers...click the quoted question above to navigate to it directly.)