Instructions to use basically-experimental/Pebble-50M-Chat-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basically-experimental/Pebble-50M-Chat-beta with Transformers:

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

pipe = pipeline("text-generation", model="basically-experimental/Pebble-50M-Chat-beta", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("basically-experimental/Pebble-50M-Chat-beta", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use basically-experimental/Pebble-50M-Chat-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "basically-experimental/Pebble-50M-Chat-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "basically-experimental/Pebble-50M-Chat-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/basically-experimental/Pebble-50M-Chat-beta

SGLang

How to use basically-experimental/Pebble-50M-Chat-beta 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 "basically-experimental/Pebble-50M-Chat-beta" \
    --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": "basically-experimental/Pebble-50M-Chat-beta",
		"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 "basically-experimental/Pebble-50M-Chat-beta" \
        --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": "basically-experimental/Pebble-50M-Chat-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use basically-experimental/Pebble-50M-Chat-beta with Docker Model Runner:
```
docker model run hf.co/basically-experimental/Pebble-50M-Chat-beta
```

Hoglet-33 commited on Sep 7

Commit

c9e0f57

verified ·

1 Parent(s): 5bebf75

SFT step 700

Browse files

Files changed (1) hide show

model.safetensors +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7a1f253d881c2b35f16ded8d9c583c59e9b3a081b47327358ffff41ad1775e0e
 size 98675424

 version https://git-lfs.github.com/spec/v1
+oid sha256:ee8766f8478377b6fe65df2f0fbd820cc63e6bbf83b21ec2f01855d51fb32bcb
 size 98675424