Instructions to use mychen76/tinyllama-colorist-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mychen76/tinyllama-colorist-v2 with Transformers:

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

pipe = pipeline("text-generation", model="mychen76/tinyllama-colorist-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mychen76/tinyllama-colorist-v2")
model = AutoModelForCausalLM.from_pretrained("mychen76/tinyllama-colorist-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mychen76/tinyllama-colorist-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mychen76/tinyllama-colorist-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mychen76/tinyllama-colorist-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mychen76/tinyllama-colorist-v2

SGLang

How to use mychen76/tinyllama-colorist-v2 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 "mychen76/tinyllama-colorist-v2" \
    --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": "mychen76/tinyllama-colorist-v2",
		"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 "mychen76/tinyllama-colorist-v2" \
        --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": "mychen76/tinyllama-colorist-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mychen76/tinyllama-colorist-v2 with Docker Model Runner:
```
docker model run hf.co/mychen76/tinyllama-colorist-v2
```

tinyllama-colorist-v2 / README.md

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metadata

license: apache-2.0

MODEL: "mychen76/tinyllama-colorist-v2" - is a finetuned TinyLlama model using color dataset.

MOTIVATION: A fun experimental model for using TinyLlama as Llama2 replacement for resource constraint environment.

PROMPT FORMAT: "<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant:""

MODEL USAGE:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline

def print_color_space(hex_color):
    def hex_to_rgb(hex_color):
        hex_color = hex_color.lstrip('#')
        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
    r, g, b = hex_to_rgb(hex_color)
    print(f'{hex_color}: \033[48;2;{r};{g};{b}m           \033[0m')

tokenizer = AutoTokenizer.from_pretrained(model_id_colorist_final)
pipe = pipeline(
    "text-generation",
    model=model_id_colorist_final,
    torch_dtype=torch.float16,
    device_map="auto",
)

from time import perf_counter
start_time = perf_counter()

prompt = formatted_prompt('give me a pure brown color')
sequences = pipe(
    prompt,
    do_sample=True,
    temperature=0.1,
    top_p=0.9,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_new_tokens=12
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

output_time = perf_counter() - start_time
print(f"Time taken for inference: {round(output_time,2)} seconds")

Result: #807070

Result: <|im_start|>user
give me a pure brown color<|im_end|>
<|im_start|>assistant: #807070<|im_end>

Time taken for inference: 0.19 seconds

Dataset: "burkelibbey/colors"