Instructions to use E6E831728/affine-recoded-minimal-code-table-free with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use E6E831728/affine-recoded-minimal-code-table-free with Transformers:

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

pipe = pipeline("text-generation", model="E6E831728/affine-recoded-minimal-code-table-free", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("E6E831728/affine-recoded-minimal-code-table-free", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use E6E831728/affine-recoded-minimal-code-table-free with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "E6E831728/affine-recoded-minimal-code-table-free"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "E6E831728/affine-recoded-minimal-code-table-free",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/E6E831728/affine-recoded-minimal-code-table-free

SGLang

How to use E6E831728/affine-recoded-minimal-code-table-free 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 "E6E831728/affine-recoded-minimal-code-table-free" \
    --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": "E6E831728/affine-recoded-minimal-code-table-free",
		"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 "E6E831728/affine-recoded-minimal-code-table-free" \
        --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": "E6E831728/affine-recoded-minimal-code-table-free",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use E6E831728/affine-recoded-minimal-code-table-free with Docker Model Runner:
```
docker model run hf.co/E6E831728/affine-recoded-minimal-code-table-free
```

E6E831728 commited on 24 days ago

Commit

cbf1b13

verified ·

1 Parent(s): b9820e0

Update README.md

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@@ -75,7 +75,7 @@ tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
 model.eval()
-prompt = "Question: What is the capital of United Kingdom?\nAnswer:"
 input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)
 with torch.no_grad():

 model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
 model.eval()
+prompt = "Question: What is the capital of UK?\nAnswer:"
 input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)
 with torch.no_grad():