Instructions to use sargurun16/VCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sargurun16/VCoder with Transformers:

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

pipe = pipeline("text-generation", model="sargurun16/VCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("sargurun16/VCoder")
model = AutoModelForCausalLM.from_pretrained("sargurun16/VCoder", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sargurun16/VCoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sargurun16/VCoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sargurun16/VCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sargurun16/VCoder

SGLang

How to use sargurun16/VCoder 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 "sargurun16/VCoder" \
    --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": "sargurun16/VCoder",
		"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 "sargurun16/VCoder" \
        --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": "sargurun16/VCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use sargurun16/VCoder with Docker Model Runner:
```
docker model run hf.co/sargurun16/VCoder
```

sargurun16 commited on Jun 15

Commit

57e2564

verified ·

1 Parent(s): 18db480

78

Browse files

Files changed (1) hide show

Run.rtf +0 -43

Run.rtf DELETED Viewed

@@ -1,43 +0,0 @@
-{\rtf1\ansi\ansicpg1252\cocoartf2867
-\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica;}
-{\colortbl;\red255\green255\blue255;}
-{\*\expandedcolortbl;;}
-\paperw11900\paperh16840\margl1440\margr1440\vieww11520\viewh8400\viewkind0
-\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
-\f0\fs24 \cf0 Step1:\
-!pip install -U transformers\
-\
-step2:\
-from transformers import pipeline\
-\
-pipe = pipeline("text-generation", model="varuneshv/VCoder")\
-messages = [\
-    \{"role": "user", "content": "Who are you?"\},\
-]\
-pipe(messages)\
-\
-step3:\
-\
-from transformers import AutoTokenizer, AutoModelForCausalLM\
-\
-tokenizer = AutoTokenizer.from_pretrained("varuneshv/VCoder")\
-\
-model = AutoModelForCausalLM.from_pretrained(\
-    "varuneshv/VCoder"\
-)\
-\
-step4:\
-\
-inputs = tokenizer(\
-    "write a python code to merge 3 arrays",\
-    return_tensors="pt"\
-)\
-\
-outputs = model.generate(\
-    **inputs,\
-    max_new_tokens=200\
-)\
-\
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))\
-}