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

Libraries

How to use omurberaisik/tiny-LLM with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")
model = PeftModel.from_pretrained(base_model, "omurberaisik/tiny-LLM")

Transformers

How to use omurberaisik/tiny-LLM with Transformers:

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

pipe = pipeline("text-generation", model="omurberaisik/tiny-LLM")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("omurberaisik/tiny-LLM", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use omurberaisik/tiny-LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omurberaisik/tiny-LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omurberaisik/tiny-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/omurberaisik/tiny-LLM

SGLang

How to use omurberaisik/tiny-LLM 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 "omurberaisik/tiny-LLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omurberaisik/tiny-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "omurberaisik/tiny-LLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omurberaisik/tiny-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use omurberaisik/tiny-LLM with Docker Model Runner:
```
docker model run hf.co/omurberaisik/tiny-LLM
```

omurberaisik commited on Apr 24

Commit

b324109

verified ·

1 Parent(s): 1384287

Training in progress, step 100

Browse files

Files changed (3) hide show

adapter_config.json +48 -0
adapter_model.safetensors +3 -0
training_args.bin +3 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,48 @@

+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
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+  "loftq_config": {},
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+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
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+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
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+    "q_proj"
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+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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