Instructions to use dickheadmorron12/cobratatellm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dickheadmorron12/cobratatellm with Transformers:

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

pipe = pipeline("text-generation", model="dickheadmorron12/cobratatellm")

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

tokenizer = AutoTokenizer.from_pretrained("dickheadmorron12/cobratatellm")
model = AutoModelForCausalLM.from_pretrained("dickheadmorron12/cobratatellm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dickheadmorron12/cobratatellm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dickheadmorron12/cobratatellm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dickheadmorron12/cobratatellm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dickheadmorron12/cobratatellm

SGLang

How to use dickheadmorron12/cobratatellm 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 "dickheadmorron12/cobratatellm" \
    --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": "dickheadmorron12/cobratatellm",
		"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 "dickheadmorron12/cobratatellm" \
        --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": "dickheadmorron12/cobratatellm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dickheadmorron12/cobratatellm with Docker Model Runner:
```
docker model run hf.co/dickheadmorron12/cobratatellm
```

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Check out the documentation for more information.

Model Card: cobratatellm

Model Details

Model Name: cobratatellm
Model Type: Language Model
Framework: Hugging Face Transformers
Architecture: GPT-3.5
Programming Languages: Python
Technologies: Next.js, React.js, TypeScript, Python, Tailwind CSS

Description

cobratatellm is a language model developed for various natural language processing tasks. It is built on the GPT-3.5 architecture and is fine-tuned for improved performance in specific domains.

Features

Supports various text generation tasks, including content creation, text completion, and more.
Understands and generates text in multiple languages.
Incorporates context and user inputs to provide contextually relevant outputs.
Utilizes the power of the Hugging Face Transformers library for seamless integration.

Intended Use Cases

Content generation for websites and applications developed using Next.js and React.js.
Text completion and augmentation in TypeScript-based projects.
Experimentation and research in natural language processing using Python.

Training Data

Pretrained on large-scale text corpora to learn grammar, language patterns, and semantics.
Fine-tuned using domain-specific data to improve performance on targeted tasks.

Limitations

May occasionally produce incorrect or nonsensical outputs.
Sensitivity to input phrasing, which can result in varying responses for similar inputs.
Limited understanding of context compared to humans.

How to Use

Install the Hugging Face Transformers library.
Load the cobratatellm model using its name or model ID.
Generate text by providing a prompt to the model's generation function.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "username/cobratatellm"  # Replace with actual model name or ID
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Once upon a time"
input_ids = tokenizer.encode(prompt, return_tensors="pt")

output = model.generate(input_ids, max_length=100)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

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