Instructions to use Junrulu/MemoChat-Vicuna-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Junrulu/MemoChat-Vicuna-33B with Transformers:

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

pipe = pipeline("text-generation", model="Junrulu/MemoChat-Vicuna-33B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Junrulu/MemoChat-Vicuna-33B")
model = AutoModelForCausalLM.from_pretrained("Junrulu/MemoChat-Vicuna-33B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Junrulu/MemoChat-Vicuna-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Junrulu/MemoChat-Vicuna-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Junrulu/MemoChat-Vicuna-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Junrulu/MemoChat-Vicuna-33B

SGLang

How to use Junrulu/MemoChat-Vicuna-33B 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 "Junrulu/MemoChat-Vicuna-33B" \
    --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": "Junrulu/MemoChat-Vicuna-33B",
		"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 "Junrulu/MemoChat-Vicuna-33B" \
        --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": "Junrulu/MemoChat-Vicuna-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Junrulu/MemoChat-Vicuna-33B with Docker Model Runner:
```
docker model run hf.co/Junrulu/MemoChat-Vicuna-33B
```

MemoChat-Vicuna-33B

Commit History

Adding `safetensors` variant of this model (#1)

00b54ff
verified

Junrulu

SFconvertbot commited on Apr 27, 2025

Update README.md

4ed0e91
verified

Junrulu commited on Mar 12, 2024

Update README.md

2bb57b8
verified

Junrulu commited on Mar 12, 2024

Upload 13 files

5c51cbd

Junrulu commited on Aug 23, 2023

initial commit

3046f91

Junrulu commited on Aug 23, 2023

Commit History

Adding `safetensors` variant of this model (#1) 00b54ff verified

Update README.md 4ed0e91 verified

Update README.md 2bb57b8 verified

Upload 13 files 5c51cbd

initial commit 3046f91

Adding `safetensors` variant of this model (#1)

00b54ff
verified

Update README.md

4ed0e91
verified

Update README.md

2bb57b8
verified

Upload 13 files

5c51cbd

initial commit

3046f91