Instructions to use alop17/Super-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alop17/Super-V1 with Transformers:

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

pipe = pipeline("text-generation", model="alop17/Super-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alop17/Super-V1")
model = AutoModelForCausalLM.from_pretrained("alop17/Super-V1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alop17/Super-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alop17/Super-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alop17/Super-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alop17/Super-V1

SGLang

How to use alop17/Super-V1 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 "alop17/Super-V1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alop17/Super-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "alop17/Super-V1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alop17/Super-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alop17/Super-V1 with Docker Model Runner:
```
docker model run hf.co/alop17/Super-V1
```

Model Card for Model ID

Model Details

Model Description

this is a coding model built for SuperIDE wepsite that i am working on

Developed by: [ALOP17]
Model type: [text gen and coding]
Language(s) (NLP): [More Information Needed]
License: [mit]]

Repository: [WORKING ON A WEPSITE FOR IT]

Uses

for coding in superIDE

Direct Use

coding & chatting

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

it's great for anything with coding but not very great for chatting or roleplaying etc.. [More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

use ie on superIDE

Summary

mostly used on superIDE

Citation [optional]

superIDE working on it

Downloads last month: 8

Safetensors

Model size

1B params

Tensor type

F16

Model tree for alop17/Super-V1

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deepseek-ai/deepseek-coder-1.3b-instruct

Finetuned

(87)

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