Instructions to use logicwahid/Kotha-S1-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use logicwahid/Kotha-S1-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="logicwahid/Kotha-S1-Instruct")

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

tokenizer = AutoTokenizer.from_pretrained("logicwahid/Kotha-S1-Instruct")
model = AutoModelForCausalLM.from_pretrained("logicwahid/Kotha-S1-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use logicwahid/Kotha-S1-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "logicwahid/Kotha-S1-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "logicwahid/Kotha-S1-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/logicwahid/Kotha-S1-Instruct

SGLang

How to use logicwahid/Kotha-S1-Instruct 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 "logicwahid/Kotha-S1-Instruct" \
    --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": "logicwahid/Kotha-S1-Instruct",
		"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 "logicwahid/Kotha-S1-Instruct" \
        --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": "logicwahid/Kotha-S1-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use logicwahid/Kotha-S1-Instruct with Docker Model Runner:
```
docker model run hf.co/logicwahid/Kotha-S1-Instruct
```

Kotha-S1-Instruct / config.json

logicwahid

Upload config.json with huggingface_hub

567e28f verified 9 days ago

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{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "model_type": "llama",
  "hidden_size": 640,
  "intermediate_size": 1706,
  "num_hidden_layers": 12,
  "num_attention_heads": 10,
  "num_key_value_heads": 10,
  "vocab_size": 32000,
  "max_position_embeddings": 512,
  "rope_theta": 10000.0,
  "rms_norm_eps": 1e-05,
  "hidden_act": "silu",
  "attention_bias": false,
  "mlp_bias": false,
  "torch_dtype": "float16",
  "bos_token_id": 2,
  "eos_token_id": 3,
  "pad_token_id": 0
}

	{
	"architectures": [
	"LlamaForCausalLM"
	],
	"model_type": "llama",
	"hidden_size": 640,
	"intermediate_size": 1706,
	"num_hidden_layers": 12,
	"num_attention_heads": 10,
	"num_key_value_heads": 10,
	"vocab_size": 32000,
	"max_position_embeddings": 512,
	"rope_theta": 10000.0,
	"rms_norm_eps": 1e-05,
	"hidden_act": "silu",
	"attention_bias": false,
	"mlp_bias": false,
	"torch_dtype": "float16",
	"bos_token_id": 2,
	"eos_token_id": 3,
	"pad_token_id": 0
	}