Instructions to use cmarkea/bloomz-3b-sft-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmarkea/bloomz-3b-sft-chat with Transformers:

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

pipe = pipeline("text-generation", model="cmarkea/bloomz-3b-sft-chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cmarkea/bloomz-3b-sft-chat")
model = AutoModelForCausalLM.from_pretrained("cmarkea/bloomz-3b-sft-chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cmarkea/bloomz-3b-sft-chat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cmarkea/bloomz-3b-sft-chat

SGLang

How to use cmarkea/bloomz-3b-sft-chat 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 "cmarkea/bloomz-3b-sft-chat" \
    --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": "cmarkea/bloomz-3b-sft-chat",
		"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 "cmarkea/bloomz-3b-sft-chat" \
        --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": "cmarkea/bloomz-3b-sft-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cmarkea/bloomz-3b-sft-chat with Docker Model Runner:
```
docker model run hf.co/cmarkea/bloomz-3b-sft-chat
```

Maximum input length

by JeromeL-DT - opened Apr 3, 2024

Discussion

JeromeL-DT

Apr 3, 2024

Hello,

Thanks for publishing your models.
How can we determine the maximum input length of any model of your collections, esp. of Bloomz-Chat collection?

(At some point, I came across tokenizer's "model_max_length" but was surprised by the value: 1000000000000000019884624838656.)

Thanks

Cyrile

Credit Mutuel Arkea org Apr 3, 2024

Hello JeromeL-DT,

Thank you for your interest in our modeling. Indeed, you will find this size limit on all models that have a relative positional encoder (as in the case of Bloom: Alibi). This is to indicate that in theory, there is no size limit. However, that's theory; in practice, we have been limited by the type of data (often chat type data has a relatively short format) and the architecture (GPU with 40 GB of VRAM). We observed a loss of modeling performance beyond a context of 5k tokens.

JeromeL-DT changed discussion status to closed Apr 4, 2024

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