Instructions to use Kaushikdebb/tinyllama_ft_full_5k_sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kaushikdebb/tinyllama_ft_full_5k_sample 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, "Kaushikdebb/tinyllama_ft_full_5k_sample")

Transformers

How to use Kaushikdebb/tinyllama_ft_full_5k_sample with Transformers:

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

pipe = pipeline("text-generation", model="Kaushikdebb/tinyllama_ft_full_5k_sample")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kaushikdebb/tinyllama_ft_full_5k_sample", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kaushikdebb/tinyllama_ft_full_5k_sample with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kaushikdebb/tinyllama_ft_full_5k_sample"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kaushikdebb/tinyllama_ft_full_5k_sample",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kaushikdebb/tinyllama_ft_full_5k_sample

SGLang

How to use Kaushikdebb/tinyllama_ft_full_5k_sample 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 "Kaushikdebb/tinyllama_ft_full_5k_sample" \
    --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": "Kaushikdebb/tinyllama_ft_full_5k_sample",
		"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 "Kaushikdebb/tinyllama_ft_full_5k_sample" \
        --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": "Kaushikdebb/tinyllama_ft_full_5k_sample",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kaushikdebb/tinyllama_ft_full_5k_sample with Docker Model Runner:
```
docker model run hf.co/Kaushikdebb/tinyllama_ft_full_5k_sample
```

tinyllama_ft_full_5k_sample

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.8222

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: 0.0001
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
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: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.9059	0.256	20	1.8654
1.8532	0.512	40	1.8407
1.8333	0.768	60	1.8315
1.8178	1.0128	80	1.8279
1.8366	1.2688	100	1.8257
1.8249	1.5248	120	1.8241
1.7824	1.7808	140	1.8230
1.8051	2.0256	160	1.8222
1.7722	2.2816	180	1.8225
1.7967	2.5376	200	1.8223
1.7583	2.7936	220	1.8222

Framework versions

PEFT 0.17.1
Transformers 4.55.4
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

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