Instructions to use danielgombas/llama_3b_step2_batch_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danielgombas/llama_3b_step2_batch_v5 with Transformers:

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

pipe = pipeline("text-generation", model="danielgombas/llama_3b_step2_batch_v5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("danielgombas/llama_3b_step2_batch_v5")
model = AutoModelForCausalLM.from_pretrained("danielgombas/llama_3b_step2_batch_v5")
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 danielgombas/llama_3b_step2_batch_v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danielgombas/llama_3b_step2_batch_v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danielgombas/llama_3b_step2_batch_v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danielgombas/llama_3b_step2_batch_v5

SGLang

How to use danielgombas/llama_3b_step2_batch_v5 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 "danielgombas/llama_3b_step2_batch_v5" \
    --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": "danielgombas/llama_3b_step2_batch_v5",
		"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 "danielgombas/llama_3b_step2_batch_v5" \
        --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": "danielgombas/llama_3b_step2_batch_v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danielgombas/llama_3b_step2_batch_v5 with Docker Model Runner:
```
docker model run hf.co/danielgombas/llama_3b_step2_batch_v5
```

llama_3b_step2_batch_v5

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3275

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: 3e-05
train_batch_size: 8
eval_batch_size: 40
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.9071	0.1363	50	0.8211
0.7063	0.2727	100	0.6140
0.5548	0.4090	150	0.5100
0.3776	0.5453	200	0.4445
0.4611	0.6817	250	0.4011
0.3701	0.8180	300	0.3742
0.325	0.9543	350	0.3529
0.2093	1.0913	400	0.3522
0.2459	1.2277	450	0.3428
0.201	1.3640	500	0.3355
0.2741	1.5003	550	0.3312
0.2596	1.6367	600	0.3283
0.2138	1.7730	650	0.3281
0.2181	1.9093	700	0.3275

Framework versions

Transformers 4.46.1
Pytorch 2.1.0+cu118
Datasets 3.0.2
Tokenizers 0.20.1

Downloads last month: 2

Safetensors

Model size

3B params

Tensor type

BF16