Instructions to use E6E831728/fixed-minimal-binary-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use E6E831728/fixed-minimal-binary-code with Transformers:

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

pipe = pipeline("text-generation", model="E6E831728/fixed-minimal-binary-code", trust_remote_code=True)

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use E6E831728/fixed-minimal-binary-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/E6E831728/fixed-minimal-binary-code

SGLang

How to use E6E831728/fixed-minimal-binary-code 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/fixed-minimal-binary-code" \
    --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/fixed-minimal-binary-code",
		"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/fixed-minimal-binary-code" \
        --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/fixed-minimal-binary-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use E6E831728/fixed-minimal-binary-code with Docker Model Runner:
```
docker model run hf.co/E6E831728/fixed-minimal-binary-code
```

E6E831728 commited on 8 days ago

Commit

d2524d0

verified ·

1 Parent(s): 667a9a7

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -68,11 +68,11 @@ 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():
-    output_ids = model.generate(input_ids, max_new_tokens=16, do_sample=False)
 print(tokenizer.decode(output_ids[0].tolist()))
 ```

 model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
 model.eval()
+prompt = "Question: What is the capital of France?\nAnswer:"
 input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)
 with torch.no_grad():
+    output_ids = model.generate(input_ids, max_new_tokens=3, do_sample=False)
 print(tokenizer.decode(output_ids[0].tolist()))
 ```