Instructions to use oddadmix/Emhotob-10M-English-MSA-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oddadmix/Emhotob-10M-English-MSA-v2 with Transformers:

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

pipe = pipeline("text-generation", model="oddadmix/Emhotob-10M-English-MSA-v2")

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

tokenizer = AutoTokenizer.from_pretrained("oddadmix/Emhotob-10M-English-MSA-v2")
model = AutoModelForCausalLM.from_pretrained("oddadmix/Emhotob-10M-English-MSA-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oddadmix/Emhotob-10M-English-MSA-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oddadmix/Emhotob-10M-English-MSA-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oddadmix/Emhotob-10M-English-MSA-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/oddadmix/Emhotob-10M-English-MSA-v2

SGLang

How to use oddadmix/Emhotob-10M-English-MSA-v2 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 "oddadmix/Emhotob-10M-English-MSA-v2" \
    --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": "oddadmix/Emhotob-10M-English-MSA-v2",
		"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 "oddadmix/Emhotob-10M-English-MSA-v2" \
        --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": "oddadmix/Emhotob-10M-English-MSA-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use oddadmix/Emhotob-10M-English-MSA-v2 with Docker Model Runner:
```
docker model run hf.co/oddadmix/Emhotob-10M-English-MSA-v2
```

Emhotob-10M-English-MSA-v2 / training_args.bin

oddadmix

English-MSA 10M SFT + card + eval

a6c0e6b verified 3 months ago

History Blame Contribute Delete

4.79 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 743f0587f0c9406a624f01a2b4fbf6ad40e0cecb6bdc73510d4ca9586127494f
Size of remote file:: 4.79 kB
SHA256:: 958bc33d180b7e4d688e2fea6e7ad9b10f6de21dc9f223da17bb2109c72a45c9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

oddadmix
/

Emhotob-10M-English-MSA-v2

Detected Pickle imports (10)

Xet Pointer Details