Instructions to use basically-ai/Pebble-10M-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basically-ai/Pebble-10M-Chat with Transformers:

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

pipe = pipeline("text-generation", model="basically-ai/Pebble-10M-Chat", trust_remote_code=True)

# Load model directly
from transformers import Pebble10MLM
model = Pebble10MLM.from_pretrained("basically-ai/Pebble-10M-Chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use basically-ai/Pebble-10M-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "basically-ai/Pebble-10M-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "basically-ai/Pebble-10M-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/basically-ai/Pebble-10M-Chat

SGLang

How to use basically-ai/Pebble-10M-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 "basically-ai/Pebble-10M-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": "basically-ai/Pebble-10M-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 "basically-ai/Pebble-10M-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": "basically-ai/Pebble-10M-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use basically-ai/Pebble-10M-Chat with Docker Model Runner:
```
docker model run hf.co/basically-ai/Pebble-10M-Chat
```

Hoglet-33 commited on about 23 hours ago

Commit

37597da

verified ·

1 Parent(s): 0b9c345

Fixed the benchmark scores.

Browse files

I fixed the benchmark scores to reflect what the model actually scored I accidentally used the same scores for base Pebble-10M.

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -55,12 +55,12 @@ Pebble-10M-Chat was evaluated on several commonsense and arithmetic benchmarks.
 | Benchmark       | Accuracy | Random Baseline |
 |-----------------|----------|-----------------|
-| PIQA            | 58.43%   | 50.00%          |
-| ARC-Easy        | 37.29%   | 25.00%          |
-| ARC-Challenge   | 18.60%   | 25.00%          |
-| HellaSwag       | 26.81%   | 25.00%          |
-| ArithMark-2.0   | 27.64%   | 25.00%          |
-| ArithMark-3.0   | 32.80%   | 25.00%          |
 ### Evaluation Notes

 | Benchmark       | Accuracy | Random Baseline |
 |-----------------|----------|-----------------|
+| PIQA            | 51.41%   | 50.00%          |
+| ARC-Easy        | 26.09%   | 25.00%          |
+| ARC-Challenge   | 20.22%   | 25.00%          |
+| HellaSwag       | 25.30%   | 25.00%          |
+| ArithMark-2.0   | 27.28%   | 25.00%          |
+| ArithMark-3.0   | 27.50%   | 25.00%          |
 ### Evaluation Notes