Instructions to use danielgombas/llama_1b_step2_batch_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danielgombas/llama_1b_step2_batch_v4 with Transformers:

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

pipe = pipeline("text-generation", model="danielgombas/llama_1b_step2_batch_v4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("danielgombas/llama_1b_step2_batch_v4")
model = AutoModelForCausalLM.from_pretrained("danielgombas/llama_1b_step2_batch_v4")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use danielgombas/llama_1b_step2_batch_v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danielgombas/llama_1b_step2_batch_v4"
# 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_1b_step2_batch_v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danielgombas/llama_1b_step2_batch_v4

SGLang

How to use danielgombas/llama_1b_step2_batch_v4 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_1b_step2_batch_v4" \
    --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_1b_step2_batch_v4",
		"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_1b_step2_batch_v4" \
        --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_1b_step2_batch_v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danielgombas/llama_1b_step2_batch_v4 with Docker Model Runner:
```
docker model run hf.co/danielgombas/llama_1b_step2_batch_v4
```

llama_1b_step2_batch_v4

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

Loss: 0.3240

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: 6
eval_batch_size: 40
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 24
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
1.0775	0.1022	50	0.9867
0.773	0.2045	100	0.7635
0.6877	0.3067	150	0.6190
0.5539	0.4090	200	0.5455
0.5489	0.5112	250	0.4832
0.5126	0.6135	300	0.4512
0.3873	0.7157	350	0.4194
0.4313	0.8180	400	0.3849
0.4021	0.9202	450	0.3643
0.2229	1.0225	500	0.3647
0.2363	1.1247	550	0.3558
0.2136	1.2270	600	0.3453
0.1827	1.3292	650	0.3391
0.2226	1.4315	700	0.3336
0.1935	1.5337	750	0.3292
0.2207	1.6360	800	0.3266
0.2445	1.7382	850	0.3254
0.1998	1.8405	900	0.3245
0.2059	1.9427	950	0.3240

Framework versions

Transformers 4.46.1
Pytorch 2.1.0+cu118
Datasets 3.0.2
Tokenizers 0.20.1

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Safetensors

Model size

1B params

Tensor type

BF16