Instructions to use RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits

SGLang

How to use RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits 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 "RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits" \
    --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": "RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits",
		"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 "RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits" \
        --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": "RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/distributed_-_gpt2-250m-convergence-test-v2-4bits
```

RichardErkhov

cherry0328 commited on Jan 7

Commit

4e94708

verified ·

1 Parent(s): a1acfb0

Update README.md (#1)

Browse files

- Update README.md (4ce60ba62dcecccd594b1ba2445edccd7d0d521c)

Co-authored-by: cherry0328 <cherry0328@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +5 -2

README.md CHANGED Viewed

@@ -1,3 +1,7 @@
 Quantization made by Richard Erkhov.
 [Github](https://github.com/RichardErkhov)
@@ -213,5 +217,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Model Card Contact
-[More Information Needed]

+---
+base_model:
+- distributed/gpt2-250m-convergence-test-v2
+---
 Quantization made by Richard Erkhov.
 [Github](https://github.com/RichardErkhov)
 ## Model Card Contact
+[More Information Needed]