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

Libraries

How to use gokaygokay/tiny_llama_chat_description_to_prompt with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gokaygokay/tiny_llama_chat_description_to_prompt")
model = AutoModelForCausalLM.from_pretrained("gokaygokay/tiny_llama_chat_description_to_prompt", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gokaygokay/tiny_llama_chat_description_to_prompt with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gokaygokay/tiny_llama_chat_description_to_prompt

SGLang

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

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

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

pip install -q -U transformers trl accelerate peft bitsandbytes

from transformers import AutoModelForCausalLM, GenerationConfig, AutoTokenizer
import torch
import os

model_id = "gokaygokay/tiny_llama_chat_description_to_prompt"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, load_in_8bit=False,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

def generate_response(user_input):

  prompt = f"<|im_start|>user\n{user_input}<|im_end|>\n<|im_start|>assistant:"

  inputs = tokenizer([prompt], return_tensors="pt")
  generation_config = GenerationConfig(penalty_alpha=0.6,do_sample = True,
      top_k=5,temperature=0.9,repetition_penalty=1.2,
      max_new_tokens=100,pad_token_id=tokenizer.eos_token_id
  )

  inputs = tokenizer(prompt, return_tensors="pt").to('cuda')

  outputs = model.generate(**inputs, generation_config=generation_config)
  print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation and attribution

This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.

@software{aydogan2024tiny_llama_chat_description_to_prompt,
  author = {Aydoğan, Gökay},
  title = {{tiny_llama_chat_description_to_prompt}},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/gokaygokay/tiny_llama_chat_description_to_prompt},
  note = {Model repository; cite the base model and upstream datasets as required.}
}

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Safetensors

Model size

1B params

Tensor type

F16

gokaygokay
/

tiny_llama_chat_description_to_prompt

Citation and attribution

Dataset used to train gokaygokay/tiny_llama_chat_description_to_prompt