Instructions to use Envoid/Dendrite-8x7Bv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Envoid/Dendrite-8x7Bv1 with Transformers:

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

pipe = pipeline("text-generation", model="Envoid/Dendrite-8x7Bv1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Envoid/Dendrite-8x7Bv1")
model = AutoModelForCausalLM.from_pretrained("Envoid/Dendrite-8x7Bv1")
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-8x7Bv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Envoid/Dendrite-8x7Bv1"
# 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-8x7Bv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Envoid/Dendrite-8x7Bv1

SGLang

How to use Envoid/Dendrite-8x7Bv1 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-8x7Bv1" \
    --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-8x7Bv1",
		"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-8x7Bv1" \
        --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-8x7Bv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Envoid/Dendrite-8x7Bv1 with Docker Model Runner:
```
docker model run hf.co/Envoid/Dendrite-8x7Bv1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is my first attempt at a qLoRA on Mixtral using the same raw text dataset as the Dendrite model on Mixtral 8x7B Instruct v0.1 with the following notable settings:

lora_r 256
lora_alpha 256
..._max_len 256
learning_rate 0.000001
num_train_epochs 2

The output is a little less dry but it still maintains the full level of functionality you would expect out of Mixtral instruct. And it still responds to the

[INST]
Do a thing
[/INST]

format. It's not a massive change in the output but I do plan to run a similar but larger data set with more epochs and a higher learning rate.

Downloads last month: 8

Safetensors

Model size

47B params

Tensor type

BF16

Model tree for Envoid/Dendrite-8x7Bv1

Quantizations

2 models