Instructions to use learner-subodh/Indic-Delivery-Rider-SLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use learner-subodh/Indic-Delivery-Rider-SLM with Transformers:

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

pipe = pipeline("text-generation", model="learner-subodh/Indic-Delivery-Rider-SLM")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("learner-subodh/Indic-Delivery-Rider-SLM")
model = AutoModelForCausalLM.from_pretrained("learner-subodh/Indic-Delivery-Rider-SLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use learner-subodh/Indic-Delivery-Rider-SLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/learner-subodh/Indic-Delivery-Rider-SLM

SGLang

How to use learner-subodh/Indic-Delivery-Rider-SLM 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 "learner-subodh/Indic-Delivery-Rider-SLM" \
    --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": "learner-subodh/Indic-Delivery-Rider-SLM",
		"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 "learner-subodh/Indic-Delivery-Rider-SLM" \
        --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": "learner-subodh/Indic-Delivery-Rider-SLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use learner-subodh/Indic-Delivery-Rider-SLM with Docker Model Runner:
```
docker model run hf.co/learner-subodh/Indic-Delivery-Rider-SLM
```

learner-subodh commited on 3 days ago

Commit

bf9bc01

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1 Parent(s): 634f5ab

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@@ -15,10 +15,27 @@ Languages: English, Hindi, Bengali, Marathi, Tamil.
 > **Experimental research model.** Trained for ~28 minutes on one T4 GPU on a small dataset. Non-English
 > domain data is **machine-translated** and has not been reviewed by native speakers. Not for payments, safety or legal advice.
 ## Architecture
-Decoder-only transformer, 10 layers, hidden 512, 8 attention heads / 4 KV heads (GQA), SwiGLU MLP (1408),
-RMSNorm, RoPE, tied embeddings, context 384. Tokenizer: SentencePiece BPE, 20000 tokens, byte fallback.
-Exports as `LlamaForCausalLM` (logits verified against the original implementation).
 Tokenizer fertility on held-out text:
 - English: 1.28 tokens/word (GPT-2 BPE: 1.30)

 > **Experimental research model.** Trained for ~28 minutes on one T4 GPU on a small dataset. Non-English
 > domain data is **machine-translated** and has not been reviewed by native speakers. Not for payments, safety or legal advice.
+## Intended Use Case
+- Offline edge execution in areas with poor connectivity.
+- Quick responses for address changes, payment status checks, COD handling, and order issues.
 ## Architecture
+- Decoder-only transformer
+- Paramters: 39.7M
+- Layers: 10
+- Hidden dimension: 512
+- Attention heads: 8
+- Number of KV Heads: 4 KV heads (GQA)
+- Activation: SwiGLU MLP (1408)
+- RMSNorm
+- Positional Embedding: RoPE
+- Tied embeddings
+- Context length = 384
+- Tokenizer: SentencePiece BPE, 20000 tokens, byte fallback
+- Exports as `LlamaForCausalLM` (logits verified against the original implementation).
 Tokenizer fertility on held-out text:
 - English: 1.28 tokens/word (GPT-2 BPE: 1.30)