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 1 month ago

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metadata

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

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