Instructions to use Ravi5528/codebharat-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ravi5528/codebharat-100m with Transformers:

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

pipe = pipeline("text-generation", model="Ravi5528/codebharat-100m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ravi5528/codebharat-100m")
model = AutoModelForCausalLM.from_pretrained("Ravi5528/codebharat-100m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ravi5528/codebharat-100m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ravi5528/codebharat-100m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ravi5528/codebharat-100m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ravi5528/codebharat-100m

SGLang

How to use Ravi5528/codebharat-100m 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 "Ravi5528/codebharat-100m" \
    --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": "Ravi5528/codebharat-100m",
		"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 "Ravi5528/codebharat-100m" \
        --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": "Ravi5528/codebharat-100m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ravi5528/codebharat-100m with Docker Model Runner:
```
docker model run hf.co/Ravi5528/codebharat-100m
```

CodeBharat-100M

CodeBharat-100M is a 100.68M parameter decoder-only Transformer pretrained from scratch on code (Python, JavaScript, TypeScript, C++, SQL, HTML/CSS, and synthetic textbooks).

Model Details

Architecture: Decoder-Only Transformer (Llama/Qwen-style: RMSNorm, RoPE, SwiGLU, Grouped-Query Attention)
Parameters: 100,679,424 (100.68M)
Vocabulary: 49,152 tokens (Byte-level BPE)
Context Window: 1,024 tokens
Training Device: NVIDIA GeForce RTX 5050 GPU
Final Validation Loss: 1.3891

Quickstart Usage

Native PyTorch / Tokenizers Usage

import torch
from tokenizers import Tokenizer
from pathlib import Path

# Load tokenizer and model weights
tokenizer = Tokenizer.from_file("tokenizer.json")
weights = torch.load("pytorch_model.bin", map_location="cuda" if torch.cuda.is_available() else "cpu")

Run Inference via CLI

python 100m-codebharat/scripts/11_generate.py --prompt "def binary_search(arr, target):"

Model Card Info

Developed by: CodeBharat Team
Model Type: Causal Language Model for Code
License: Apache 2.0

Downloads last month: 44

Safetensors

Model size

0.1B params

Tensor type

F32