Instructions to use AnveshAI/AnveshAI-Ritchie-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnveshAI/AnveshAI-Ritchie-0.1B with Transformers:

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

pipe = pipeline("text-generation", model="AnveshAI/AnveshAI-Ritchie-0.1B")

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

tokenizer = AutoTokenizer.from_pretrained("AnveshAI/AnveshAI-Ritchie-0.1B")
model = AutoModelForCausalLM.from_pretrained("AnveshAI/AnveshAI-Ritchie-0.1B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnveshAI/AnveshAI-Ritchie-0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnveshAI/AnveshAI-Ritchie-0.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnveshAI/AnveshAI-Ritchie-0.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AnveshAI/AnveshAI-Ritchie-0.1B

SGLang

How to use AnveshAI/AnveshAI-Ritchie-0.1B 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 "AnveshAI/AnveshAI-Ritchie-0.1B" \
    --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": "AnveshAI/AnveshAI-Ritchie-0.1B",
		"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 "AnveshAI/AnveshAI-Ritchie-0.1B" \
        --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": "AnveshAI/AnveshAI-Ritchie-0.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AnveshAI/AnveshAI-Ritchie-0.1B with Docker Model Runner:
```
docker model run hf.co/AnveshAI/AnveshAI-Ritchie-0.1B
```

developeranveshraman commited on 7 days ago

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5566d01

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@@ -19,17 +19,27 @@ model-index:
 - name: AnveshAI-Ritchie-0.1B
   results: []
 ---
-<p align="center">
-  <img src="logo.png" alt="AnveshAI Logo" width="260">
-</p>
-<p align="center">
-  <img src="https://img.shields.io/badge/Parameters-100.7M-blue" alt="Parameters"> <img src="https://img.shields.io/badge/Architecture-LLaMA-green" alt="Architecture"> <img src="https://img.shields.io/badge/Language-C-orange" alt="Language"> <img src="https://img.shields.io/badge/Context-2048_tokens-purple" alt="Context Length"> <img src="https://img.shields.io/badge/Precision-FP16-red" alt="Precision">
-</p>
 **Ritchie** is a compact 100.7M-parameter language model purpose-built for **C programming** — named after [Dennis Ritchie](https://en.wikipedia.org/wiki/Dennis_Ritchie), the creator of the C language. Built on the LLaMA architecture, Ritchie is designed to generate C code from natural language task descriptions.
-> **Small enough to run on any device — including edge devices like smartphones, tablets, Raspberry Pi, and other embedded hardware.** At just ~200MB (FP16), Ritchie brings C code generation to devices where larger models simply can't run. No GPU required — it runs comfortably on CPU.
 ---

 - name: AnveshAI-Ritchie-0.1B
   results: []
 ---
+<div align="center">
+  <picture>
+    <img src="logo.png" width="25%" alt="AnveshAI Ritchie 0.1B">
+  </picture>
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/AnveshAI" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AnveshAI-ffc107?color=ffc107&logoColor=white"/></a>
+  <a href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue?logoColor=white"/></a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a herf="#"><img src="https://img.shields.io/badge/Parameters-100.7M-blue" alt="Parameters"></a>
+  <img src="https://img.shields.io/badge/Architecture-LLaMA-green" alt="Architecture">
+  <img src="https://img.shields.io/badge/Language-C-orange" alt="Language">
+  <img src="https://img.shields.io/badge/Context-2048_tokens-purple" alt="Context Length">
+  <img src="https://img.shields.io/badge/Precision-FP16-red" alt="Precision">
+</div>
 **Ritchie** is a compact 100.7M-parameter language model purpose-built for **C programming** — named after [Dennis Ritchie](https://en.wikipedia.org/wiki/Dennis_Ritchie), the creator of the C language. Built on the LLaMA architecture, Ritchie is designed to generate C code from natural language task descriptions.
+**Small enough to run on any device — including edge devices like smartphones, tablets, Raspberry Pi, and other embedded hardware.** At just ~200MB (FP16), Ritchie brings C code generation to devices where larger models simply can't run. No GPU required — it runs comfortably on CPU.
 ---