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

Libraries

How to use KnutJaegersberg/black_goo_recipe_a with Transformers:

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

pipe = pipeline("text-generation", model="KnutJaegersberg/black_goo_recipe_a", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KnutJaegersberg/black_goo_recipe_a with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KnutJaegersberg/black_goo_recipe_a

SGLang

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

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

Prompt template

### Instruction:

What is the difference between operant and classical conditioning? 

### Response:

Classical conditioning is a learning process that occurs when an organism is exposed to a stimulus that elicits a response. This response is referred to as a conditioned response (CR). The stimulus that elicits the response is called the conditioned stimulus (CS). The organism's response to the CS is called the unconditioned response (UR).

Operant conditioning is a learning process that occurs when an organism is exposed to a stimulus that elicits a response. This response is referred to as an operant response (OR). The stimulus that elicits the response is called the operant stimulus (OS). The organism's response to the OS is called the operant response (OR).

Classical conditioning and operant conditioning are two different types of learning. Classical conditioning involves the association of a CS with a UR, while operant conditioning involves the association of an OS with an OR.

In classical conditioning, the CS is paired with the UR over time, resulting in the UR becoming conditioned to the CS. This process is referred to as Pavlovian conditioning. In operant conditioning, the OS is paired with the OR over time, resulting in the OR becoming conditioned to the OS. This process is referred to as instrumental conditioning.

Both classical conditioning and operant conditioning are important in learning and behavior modification. However, they differ in the way they are used and the types of responses they elicit.

In conclusion, classical conditioning and operant conditioning are two different types of learning that occur when an organism is exposed to a stimulus that elicits a response. Classical conditioning involves the association of a CS with a UR, while operant conditioning involves the association of an OS with an OR.

Downloads last month: 144

KnutJaegersberg
/

black_goo_recipe_a

Spaces using KnutJaegersberg/black_goo_recipe_a 28