Instructions to use IvanD2002/Stuco_Task_Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IvanD2002/Stuco_Task_Generator with Transformers:

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

pipe = pipeline("text-generation", model="IvanD2002/Stuco_Task_Generator", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IvanD2002/Stuco_Task_Generator", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("IvanD2002/Stuco_Task_Generator", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IvanD2002/Stuco_Task_Generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IvanD2002/Stuco_Task_Generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IvanD2002/Stuco_Task_Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IvanD2002/Stuco_Task_Generator

SGLang

How to use IvanD2002/Stuco_Task_Generator 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 "IvanD2002/Stuco_Task_Generator" \
    --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": "IvanD2002/Stuco_Task_Generator",
		"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 "IvanD2002/Stuco_Task_Generator" \
        --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": "IvanD2002/Stuco_Task_Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IvanD2002/Stuco_Task_Generator with Docker Model Runner:
```
docker model run hf.co/IvanD2002/Stuco_Task_Generator
```

Stuco_Task_Generator / config.json

IvanD2002

Update config.json

f77da76 verified over 2 years ago

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	{
	"alibi": false,
	"apply_residual_connection_post_layernorm": false,
	"architectures": [
	"FalconForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_falcon.FalconConfig",
	"AutoModel": "modeling_falcon.FalconModel",
	"AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification",
	"AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",
	"AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering",
	"AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM"
	},
	"bias": false,
	"bos_token_id": 11,
	"eos_token_id": 11,
	"hidden_dropout": 0.0,
	"hidden_size": 4544,
	"initializer_range": 0.02,
	"layer_norm_epsilon": 1e-05,
	"max_position_embeddings": 2048,
	"model_type": "falcon",
	"multi_query": true,
	"new_decoder_architecture": false,
	"num_attention_heads": 71,
	"num_hidden_layers": 32,
	"num_kv_heads": 71,
	"parallel_attn": true,
	"quantization_config": {
	"_load_in_4bit": true,
	"_load_in_8bit": false,
	"bnb_4bit_compute_dtype": "float32",
	"bnb_4bit_quant_type": "fp4",
	"bnb_4bit_use_double_quant": false,
	"llm_int8_enable_fp32_cpu_offload": false,
	"llm_int8_has_fp16_weight": false,
	"llm_int8_skip_modules": null,
	"llm_int8_threshold": 6.0,
	"load_in_4bit": true,
	"load_in_8bit": false,
	"quant_method": "bitsandbytes"
	},
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"torch_dtype": "float16",
	"transformers_version": "4.38.2",
	"use_cache": true,
	"vocab_size": 65024
	}