Instructions to use HarrySoteriou/smoke-test-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HarrySoteriou/smoke-test-output with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "HarrySoteriou/smoke-test-output")

Transformers

How to use HarrySoteriou/smoke-test-output with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HarrySoteriou/smoke-test-output")
model = AutoModelForCausalLM.from_pretrained("HarrySoteriou/smoke-test-output")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HarrySoteriou/smoke-test-output with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HarrySoteriou/smoke-test-output

SGLang

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

Docker Model Runner
How to use HarrySoteriou/smoke-test-output with Docker Model Runner:
```
docker model run hf.co/HarrySoteriou/smoke-test-output
```

See axolotl config

axolotl version: 0.14.0

base_model: unsloth/Llama-3.2-1B-Instruct
model_type: AutoModelForCausalLM
adapter: qlora
load_in_4bit: true
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: true
bnb_4bit_compute_dtype: bfloat16
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
datasets:
- path: yahma/alpaca-cleaned
  type: alpaca
sequence_len: 2048
micro_batch_size: 1
gradient_accumulation_steps: 4
num_epochs: 1
max_steps: 10
learning_rate: 0.0002
optimizer: adamw_bnb_8bit
lr_scheduler_type: cosine
warmup_steps: 10
flash_attention: false
gradient_checkpointing: true
bf16: auto
logging_steps: 10
save_steps: 100
output_dir: /app/results/7a775f96-133a-44da-975d-d4875774c579
push_to_hub: true
hub_model_id: HarrySoteriou/smoke-test-output
hf_use_auth_token: true

smoke-test-output

This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct on the yahma/alpaca-cleaned dataset.

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: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 10

Training results

Framework versions

PEFT 0.18.1
Transformers 4.57.6
Pytorch 2.9.1+cu128
Datasets 4.3.0
Tokenizers 0.22.2

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Model tree for HarrySoteriou/smoke-test-output

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

unsloth/Llama-3.2-1B-Instruct

Adapter

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this model

HarrySoteriou
/

smoke-test-output

smoke-test-output

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for HarrySoteriou/smoke-test-output

Dataset used to train HarrySoteriou/smoke-test-output