Instructions to use SystemAdmin123/tiny-random-LlamaForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SystemAdmin123/tiny-random-LlamaForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="SystemAdmin123/tiny-random-LlamaForCausalLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SystemAdmin123/tiny-random-LlamaForCausalLM")
model = AutoModelForCausalLM.from_pretrained("SystemAdmin123/tiny-random-LlamaForCausalLM")
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 SystemAdmin123/tiny-random-LlamaForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SystemAdmin123/tiny-random-LlamaForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SystemAdmin123/tiny-random-LlamaForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SystemAdmin123/tiny-random-LlamaForCausalLM

SGLang

How to use SystemAdmin123/tiny-random-LlamaForCausalLM 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 "SystemAdmin123/tiny-random-LlamaForCausalLM" \
    --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": "SystemAdmin123/tiny-random-LlamaForCausalLM",
		"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 "SystemAdmin123/tiny-random-LlamaForCausalLM" \
        --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": "SystemAdmin123/tiny-random-LlamaForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SystemAdmin123/tiny-random-LlamaForCausalLM with Docker Model Runner:
```
docker model run hf.co/SystemAdmin123/tiny-random-LlamaForCausalLM
```

SystemAdmin123 commited on Feb 4, 2025

Commit

2766622

verified ·

1 Parent(s): 8395ef5

Training in progress, step 400

Browse files

Files changed (3) hide show

axolotl_config.yaml +7 -3
model.safetensors +1 -1
training_args.bin +2 -2

axolotl_config.yaml CHANGED Viewed

@@ -13,7 +13,9 @@ datasets:
     no_input_format: '{instruction}'
     system_format: '{system}'
     system_prompt: ''
-eval_steps: 20
 flash_attention: true
 gpu_memory_limit: 80GiB
 group_by_length: true
@@ -23,17 +25,19 @@ learning_rate: 0.0002
 logging_steps: 10
 lr_scheduler: cosine
 max_steps: 2500
-micro_batch_size: 2
 model_type: AutoModelForCausalLM
 num_epochs: 100
 optimizer: adamw_bnb_8bit
 output_dir: /root/.sn56/axolotl/outputs/tiny-random-LlamaForCausalLM
 pad_to_sequence_len: true
 resize_token_embeddings_to_32x: false
-save_steps: 40
 save_total_limit: 1
 sequence_len: 2048
 tokenizer_type: LlamaTokenizerFast
 trust_remote_code: true
 val_set_size: 0.1
 wandb_entity: ''

     no_input_format: '{instruction}'
     system_format: '{system}'
     system_prompt: ''
+device_map: auto
+eval_sample_packing: false
+eval_steps: 200
 flash_attention: true
 gpu_memory_limit: 80GiB
 group_by_length: true
 logging_steps: 10
 lr_scheduler: cosine
 max_steps: 2500
+micro_batch_size: 4
 model_type: AutoModelForCausalLM
 num_epochs: 100
 optimizer: adamw_bnb_8bit
 output_dir: /root/.sn56/axolotl/outputs/tiny-random-LlamaForCausalLM
 pad_to_sequence_len: true
 resize_token_embeddings_to_32x: false
+sample_packing: false
+save_steps: 400
 save_total_limit: 1
 sequence_len: 2048
 tokenizer_type: LlamaTokenizerFast
+torch_dtype: bf16
 trust_remote_code: true
 val_set_size: 0.1
 wandb_entity: ''

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