Instructions to use OnlyCheeini/Llama-Discore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OnlyCheeini/Llama-Discore with Transformers:

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

pipe = pipeline("text-generation", model="OnlyCheeini/Llama-Discore")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OnlyCheeini/Llama-Discore", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OnlyCheeini/Llama-Discore with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OnlyCheeini/Llama-Discore

SGLang

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

Docker Model Runner
How to use OnlyCheeini/Llama-Discore with Docker Model Runner:
```
docker model run hf.co/OnlyCheeini/Llama-Discore
```

Llama-Discore / README.md

OnlyCheeini

Update README.md

ec45fd6 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

1.08 kB

	---
	tags:
	- autotrain
	- text-generation
	widget:
	- text: 'Hello '
	license: other
	library_name: transformers
	pipeline_tag: text2text-generation
	---

	# Model Trained Using AutoTrain

	This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
	# Architecture

	This model finetuned version of llama-2-7b
	# Usage

	```python

	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_path = "OmlyCheeini/Llama-Discore"

	tokenizer = AutoTokenizer.from_pretrained(model_path)
	model = AutoModelForCausalLM.from_pretrained(
	model_path,
	device_map="auto",
	torch_dtype='auto'
	).eval()

	# Prompt content: "hi"
	messages = [
	{"role": "user", "content": "hi"}
	]

	input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
	output_ids = model.generate(input_ids.to('cuda'))
	response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

	# Model response: "Hello! How can I assist you today?"
	print(response)
	```