Instructions to use Envoid/Dendrite-L3-10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Envoid/Dendrite-L3-10B with Transformers:

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

pipe = pipeline("text-generation", model="Envoid/Dendrite-L3-10B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Envoid/Dendrite-L3-10B")
model = AutoModelForCausalLM.from_pretrained("Envoid/Dendrite-L3-10B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Envoid/Dendrite-L3-10B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Envoid/Dendrite-L3-10B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Envoid/Dendrite-L3-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Envoid/Dendrite-L3-10B

SGLang

How to use Envoid/Dendrite-L3-10B 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 "Envoid/Dendrite-L3-10B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Envoid/Dendrite-L3-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Envoid/Dendrite-L3-10B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Envoid/Dendrite-L3-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Envoid/Dendrite-L3-10B with Docker Model Runner:
```
docker model run hf.co/Envoid/Dendrite-L3-10B
```

Envoid commited on May 6, 2024

Commit

7b1dbe9

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Update README.md

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@@ -58,4 +58,6 @@ It has been tested in RP, assistant and creative writing use cases and at a quic
 Training was done using [qlora-pipe](https://github.com/tdrussell/qlora-pipe)
-exl2 RPCAL care of [Qaunt Cartel](https://huggingface.co/Quant-Cartel/Dendrite-L3-10B-exl2-rpcal)

 Training was done using [qlora-pipe](https://github.com/tdrussell/qlora-pipe)
+exl2 RPCAL care of [Qaunt Cartel](https://huggingface.co/Quant-Cartel/Dendrite-L3-10B-exl2-rpcal)
+GGUFs care of [Quant Cartel](https://huggingface.co/Quant-Cartel/Dendrite-L3-10B-iMat-GGUF)