Instructions to use Sujith2121/tinyllama-dora-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sujith2121/tinyllama-dora-model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "Sujith2121/tinyllama-dora-model")

Transformers

How to use Sujith2121/tinyllama-dora-model with Transformers:

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

pipe = pipeline("text-generation", model="Sujith2121/tinyllama-dora-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sujith2121/tinyllama-dora-model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sujith2121/tinyllama-dora-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sujith2121/tinyllama-dora-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sujith2121/tinyllama-dora-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sujith2121/tinyllama-dora-model

SGLang

How to use Sujith2121/tinyllama-dora-model 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 "Sujith2121/tinyllama-dora-model" \
    --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": "Sujith2121/tinyllama-dora-model",
		"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 "Sujith2121/tinyllama-dora-model" \
        --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": "Sujith2121/tinyllama-dora-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sujith2121/tinyllama-dora-model with Docker Model Runner:
```
docker model run hf.co/Sujith2121/tinyllama-dora-model
```

tinyllama-dora-model / README.md

Sujith2121

Update README.md

afa8436 verified about 2 months ago

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	---
	library_name: peft
	license: apache-2.0
	base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
	tags:
	- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
	- dora
	- qlora
	- transformers
	- text-generation
	pipeline_tag: text-generation

	model-index:
	- name: tinyllama-dora-model
	results:
	- task:
	type: text-generation
	dataset:
	name: mlabonne/guanaco-llama2-1k
	type: instruction-tuning
	metrics:
	- type: loss
	value: 1.5644
	name: validation_loss
	---

	# tinyllama-dora-model

	## Model Description

	This model is a parameter-efficient fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using DoRA combined with 4-bit quantization.

	---

	## Key Features

	* Base Model: TinyLlama-1.1B-Chat
	* Fine-tuning Method: DoRA
	* Quantization: 4-bit
	* Framework: Transformers + PEFT

	---

	## Intended Use

	* Instruction-based text generation
	* Conversational AI
	* Research and experimentation

	---

	## Limitations

	* Small dataset (1k samples)
	* May produce incorrect outputs

	---

	## Dataset

	mlabonne/guanaco-llama2-1k

	---

	## Training Details

	* Learning Rate: 5e-5
	* Batch Size: 2
	* Epochs: 1

	---

	## Results

	Validation Loss: 1.5644
	Perplexity = exp(loss)

	---

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import PeftModel

	base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
	adapter_model = "Sujith2121/tinyllama-dora-model"

	tokenizer = AutoTokenizer.from_pretrained(adapter_model)

	model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
	model = PeftModel.from_pretrained(model, adapter_model)

	prompt = "Explain Docker simply"

	inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
	outputs = model.generate(**inputs, max_new_tokens=100)

	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	---

	## License

	Apache 2.0