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

Libraries

How to use GeneZC/MiniChat-3B with Transformers:

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

pipe = pipeline("text-generation", model="GeneZC/MiniChat-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B")
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B")
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 GeneZC/MiniChat-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GeneZC/MiniChat-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GeneZC/MiniChat-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GeneZC/MiniChat-3B

SGLang

How to use GeneZC/MiniChat-3B 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 "GeneZC/MiniChat-3B" \
    --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": "GeneZC/MiniChat-3B",
		"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 "GeneZC/MiniChat-3B" \
        --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": "GeneZC/MiniChat-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GeneZC/MiniChat-3B with Docker Model Runner:
```
docker model run hf.co/GeneZC/MiniChat-3B
```

shakhal commited on Nov 25, 2023

Commit

576552f

1 Parent(s): 3cf2a82

Update README.md

Browse files

Fix for running on cpu

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -34,8 +34,8 @@ tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False)
 # GPU.
 model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
 # CPU.
-# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval()
 conv = get_default_conv_template("minichat")
 question = "Implement a program to find the common elements in two arrays without using any extra data structures."
@@ -44,7 +44,7 @@ conv.append_message(conv.roles[1], None)
 prompt = conv.get_prompt()
 input_ids = tokenizer([prompt]).input_ids
 output_ids = model.generate(
-    torch.as_tensor(input_ids).cuda(),
     do_sample=True,
     temperature=0.7,
     max_new_tokens=1024,

 # GPU.
 model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
 # CPU.
+# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float32).eval()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 conv = get_default_conv_template("minichat")
 question = "Implement a program to find the common elements in two arrays without using any extra data structures."
 prompt = conv.get_prompt()
 input_ids = tokenizer([prompt]).input_ids
 output_ids = model.generate(
+    torch.as_tensor(input_ids).to(device),
     do_sample=True,
     temperature=0.7,
     max_new_tokens=1024,