Instructions to use maksymK4198/09f393900fba72ef with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maksymK4198/09f393900fba72ef with Transformers:

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

pipe = pipeline("text-generation", model="maksymK4198/09f393900fba72ef")
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("maksymK4198/09f393900fba72ef")
model = AutoModelForCausalLM.from_pretrained("maksymK4198/09f393900fba72ef", 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 maksymK4198/09f393900fba72ef with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maksymK4198/09f393900fba72ef"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maksymK4198/09f393900fba72ef",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maksymK4198/09f393900fba72ef

SGLang

How to use maksymK4198/09f393900fba72ef 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 "maksymK4198/09f393900fba72ef" \
    --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": "maksymK4198/09f393900fba72ef",
		"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 "maksymK4198/09f393900fba72ef" \
        --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": "maksymK4198/09f393900fba72ef",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maksymK4198/09f393900fba72ef with Docker Model Runner:
```
docker model run hf.co/maksymK4198/09f393900fba72ef
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Name

Community-shared Affine (SN120) checkpoint.

Field	Value
Hotkey	`5FUNwNqtpQiQTRrEevq1UngxtpyFXM8sorSwojedPccVMo7R`
Model digest	`3848e11df41dd533d8e57cd2aa2baab2341ca0006313ef3da1c814ff9102d49c`
Size	69.34 GB
Files	24

Published by the Affine Community Dashboard. Access is gated — request access on Hugging Face to download.

Downloads last month: -

Safetensors

Model size

35B params

Tensor type

BF16