Instructions to use helloadhavan/llara1.0-100M-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use helloadhavan/llara1.0-100M-base with Transformers:

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

pipe = pipeline("text-generation", model="helloadhavan/llara1.0-100M-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("helloadhavan/llara1.0-100M-base")
model = AutoModelForCausalLM.from_pretrained("helloadhavan/llara1.0-100M-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use helloadhavan/llara1.0-100M-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "helloadhavan/llara1.0-100M-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "helloadhavan/llara1.0-100M-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/helloadhavan/llara1.0-100M-base

SGLang

How to use helloadhavan/llara1.0-100M-base 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 "helloadhavan/llara1.0-100M-base" \
    --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": "helloadhavan/llara1.0-100M-base",
		"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 "helloadhavan/llara1.0-100M-base" \
        --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": "helloadhavan/llara1.0-100M-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use helloadhavan/llara1.0-100M-base with Docker Model Runner:
```
docker model run hf.co/helloadhavan/llara1.0-100M-base
```

helloadhavan commited on Jun 19

Commit

c91529d

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1 Parent(s): fc8344f

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@@ -14,6 +14,9 @@ pipeline_tag: text-generation
 # Llara
 Llara is a 91.4M parameter autoregressive language model trained from scratch on English web text. It follows the GPT-2 Small architecture and is trained entirely from random initialisation — no pretrained weights, no distillation, no fine-tuning of an existing model.
 but it does use GPT's tokenizer

 # Llara
+<img src="data:image/svg+xml;base64,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">
 Llara is a 91.4M parameter autoregressive language model trained from scratch on English web text. It follows the GPT-2 Small architecture and is trained entirely from random initialisation — no pretrained weights, no distillation, no fine-tuning of an existing model.
 but it does use GPT's tokenizer