Instructions to use DevQuasar-3/llama3_8b_chat_brainstorm_plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevQuasar-3/llama3_8b_chat_brainstorm_plus with Transformers:

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

pipe = pipeline("text-generation", model="DevQuasar-3/llama3_8b_chat_brainstorm_plus")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DevQuasar-3/llama3_8b_chat_brainstorm_plus")
model = AutoModelForCausalLM.from_pretrained("DevQuasar-3/llama3_8b_chat_brainstorm_plus", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DevQuasar-3/llama3_8b_chat_brainstorm_plus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevQuasar-3/llama3_8b_chat_brainstorm_plus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DevQuasar-3/llama3_8b_chat_brainstorm_plus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DevQuasar-3/llama3_8b_chat_brainstorm_plus

SGLang

How to use DevQuasar-3/llama3_8b_chat_brainstorm_plus 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 "DevQuasar-3/llama3_8b_chat_brainstorm_plus" \
    --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": "DevQuasar-3/llama3_8b_chat_brainstorm_plus",
		"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 "DevQuasar-3/llama3_8b_chat_brainstorm_plus" \
        --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": "DevQuasar-3/llama3_8b_chat_brainstorm_plus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DevQuasar-3/llama3_8b_chat_brainstorm_plus with Docker Model Runner:
```
docker model run hf.co/DevQuasar-3/llama3_8b_chat_brainstorm_plus
```

'Make knowledge free for everyone'

Brainstom Plus

Model intention

Brainstorm facilitates idea exploration through interaction with a Language Model (LLM). Rather than providing direct answers, the model engages in a dialogue with users, offering probing questions aimed at fostering deeper contemplation and consideration of various facets of their ideas.

Examples

Inference code

https://github.com/csabakecskemeti/ai_utils/blob/main/brainstorm_inference.py

Usage

python brainstorm_inference.py DevQuasar/llama3_8b_chat_brainstorm_plus

I'm doing this to 'Make knowledge free for everyone', using my personal time and resources.

If you want to support my efforts please visit my ko-fi page: https://ko-fi.com/devquasar

Also feel free to visit my website https://devquasar.com/

Downloads last month: 20

Safetensors

Model size

8B params

Tensor type

F32

Model tree for DevQuasar-3/llama3_8b_chat_brainstorm_plus

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

(611)

this model

DevQuasar-3
/

llama3_8b_chat_brainstorm_plus