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
```

Sujith2121 commited on Apr 1

Commit

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@@ -3,61 +3,104 @@ 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
-- lora
-- transformers
 pipeline_tag: text-generation
 model-index:
-- name: tinyllama-dora-model
-  results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 # tinyllama-dora-model
-This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.5644
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 2
-- eval_batch_size: 2
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 1
-- mixed_precision_training: Native AMP
-### Training results
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 1.5365        | 1.0   | 450  | 1.5644          |
-### Framework versions
-- PEFT 0.18.2.dev0
-- Transformers 5.0.0
-- Pytorch 2.10.0+cu128
-- Datasets 4.8.3
-- Tokenizers 0.22.2

 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