Instructions to use zarko1321/Bitsy500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zarko1321/Bitsy500M with Transformers:

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

pipe = pipeline("text-generation", model="zarko1321/Bitsy500M", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("zarko1321/Bitsy500M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zarko1321/Bitsy500M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zarko1321/Bitsy500M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zarko1321/Bitsy500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zarko1321/Bitsy500M

SGLang

How to use zarko1321/Bitsy500M 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 "zarko1321/Bitsy500M" \
    --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": "zarko1321/Bitsy500M",
		"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 "zarko1321/Bitsy500M" \
        --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": "zarko1321/Bitsy500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zarko1321/Bitsy500M with Docker Model Runner:
```
docker model run hf.co/zarko1321/Bitsy500M
```

Bitsy500M / validation_report.json

zarko1321

Upload 23 files

cdf6b36 verified 7 days ago

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{
  "checks": {
    "tokenizer_exact_roundtrips": true,
    "special_ids_distinct_and_persistent": true,
    "chat_template_real_newlines_and_generation_prefix": true,
    "causality_and_multitoken_and_single_token_KV_cache": true,
    "left_padding_and_beam_generation": true,
    "labels_shifted_exactly_once_and_minus100_mask": true,
    "tiny_model_overfit_and_checkpointed_backward": {
      "initial": 10.42803955078125,
      "final": 5.2051544189453125,
      "updates": 15
    },
    "tiny_save_reload_exact_logits": true,
    "full_model_load_no_missing_unexpected_or_mismatched_weights": true,
    "full_model_parameter_count_and_tied_embeddings": 509943936,
    "full_BF16_save_reload_exact_logits": true,
    "full_model_cached_generation_smoke": true,
    "full_model_BF16_forward_backward_all_gradients_finite_nonzero": {
      "loss": 11.141535758972168,
      "parameter_tensors": 299
    },
    "pretraining_runner_tiny_CPU_two_updates_and_exact_checkpoint_resume": true,
    "replacement_first_shard_tensor_bytes_match_original_backup": true
  },
  "hardware": "CPU; no CUDA/Unsloth Studio validation performed",
  "passed": true,
  "limitations": [
    "Full-sized model is randomly initialized, not pretrained.",
    "CUDA throughput, memory fit, long-sequence training, and Unsloth Studio integration were not tested.",
    "No tested context-extension recipe, chat skill, or coding skill is claimed.",
    "Tiny model overfit is a plumbing test, not a capability result."
  ],
  "test_dependencies": {
    "torch": "2.6.0+cpu",
    "transformers": "4.57.1",
    "datasets": "4.4.1"
  }
}

	{
	"checks": {
	"tokenizer_exact_roundtrips": true,
	"special_ids_distinct_and_persistent": true,
	"chat_template_real_newlines_and_generation_prefix": true,
	"causality_and_multitoken_and_single_token_KV_cache": true,
	"left_padding_and_beam_generation": true,
	"labels_shifted_exactly_once_and_minus100_mask": true,
	"tiny_model_overfit_and_checkpointed_backward": {
	"initial": 10.42803955078125,
	"final": 5.2051544189453125,
	"updates": 15
	},
	"tiny_save_reload_exact_logits": true,
	"full_model_load_no_missing_unexpected_or_mismatched_weights": true,
	"full_model_parameter_count_and_tied_embeddings": 509943936,
	"full_BF16_save_reload_exact_logits": true,
	"full_model_cached_generation_smoke": true,
	"full_model_BF16_forward_backward_all_gradients_finite_nonzero": {
	"loss": 11.141535758972168,
	"parameter_tensors": 299
	},
	"pretraining_runner_tiny_CPU_two_updates_and_exact_checkpoint_resume": true,
	"replacement_first_shard_tensor_bytes_match_original_backup": true
	},
	"hardware": "CPU; no CUDA/Unsloth Studio validation performed",
	"passed": true,
	"limitations": [
	"Full-sized model is randomly initialized, not pretrained.",
	"CUDA throughput, memory fit, long-sequence training, and Unsloth Studio integration were not tested.",
	"No tested context-extension recipe, chat skill, or coding skill is claimed.",
	"Tiny model overfit is a plumbing test, not a capability result."
	],
	"test_dependencies": {
	"torch": "2.6.0+cpu",
	"transformers": "4.57.1",
	"datasets": "4.4.1"
	}
	}