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

File size: 437 Bytes

6af0329

{
  "architectures": [
    "CodeBharatForCausalLM"
  ],
  "model_type": "llama",
  "vocab_size": 49152,
  "hidden_size": 768,
  "num_hidden_layers": 10,
  "num_attention_heads": 12,
  "num_key_value_heads": 4,
  "intermediate_size": 2048,
  "max_position_embeddings": 1024,
  "rms_norm_eps": 1e-06,
  "rope_theta": 10000.0,
  "tie_word_embeddings": true,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.40.0"
}