Instructions to use MultiTrickFox/bloom-2b5_Zen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MultiTrickFox/bloom-2b5_Zen with Transformers:

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

pipe = pipeline("text-generation", model="MultiTrickFox/bloom-2b5_Zen")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
model = AutoModelForCausalLM.from_pretrained("MultiTrickFox/bloom-2b5_Zen", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MultiTrickFox/bloom-2b5_Zen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MultiTrickFox/bloom-2b5_Zen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MultiTrickFox/bloom-2b5_Zen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MultiTrickFox/bloom-2b5_Zen

SGLang

How to use MultiTrickFox/bloom-2b5_Zen 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 "MultiTrickFox/bloom-2b5_Zen" \
    --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": "MultiTrickFox/bloom-2b5_Zen",
		"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 "MultiTrickFox/bloom-2b5_Zen" \
        --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": "MultiTrickFox/bloom-2b5_Zen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MultiTrickFox/bloom-2b5_Zen with Docker Model Runner:
```
docker model run hf.co/MultiTrickFox/bloom-2b5_Zen
```

MultiTrickFox commited on Sep 8, 2022

Commit

6822221

1 Parent(s): a80e3ea

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -4

README.md CHANGED Viewed

@@ -18,15 +18,14 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
 model = AutoModelForCausalLM.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
-tokenizer.pad_token_id = tokenizer.eos_token_id
 generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
 inp = [ """Today""", """Yesterday""" ]
 out = generator(
-    inp, do_sample=True,
     temperature=.7,
     typical_p=.6,
     #top_p=.9,

 tokenizer = AutoTokenizer.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
 model = AutoModelForCausalLM.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
 generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
 inp = [ """Today""", """Yesterday""" ]
 out = generator(
+    inp,
+    do_sample=True,
     temperature=.7,
     typical_p=.6,
     #top_p=.9,