Text Generation
Transformers
Safetensors
English
llama
commonsense-reasoning
winoGrande
fine-tuned
reasoning
text-generation-inference
Instructions to use Yuvrajxms09/Monad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yuvrajxms09/Monad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Yuvrajxms09/Monad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Yuvrajxms09/Monad") model = AutoModelForCausalLM.from_pretrained("Yuvrajxms09/Monad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Yuvrajxms09/Monad with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Yuvrajxms09/Monad" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Yuvrajxms09/Monad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Yuvrajxms09/Monad
- SGLang
How to use Yuvrajxms09/Monad 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 "Yuvrajxms09/Monad" \ --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": "Yuvrajxms09/Monad", "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 "Yuvrajxms09/Monad" \ --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": "Yuvrajxms09/Monad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Yuvrajxms09/Monad with Docker Model Runner:
docker model run hf.co/Yuvrajxms09/Monad
Download config.json from Yuvrajxms09/Monad: direct link, hf CLI and curl.
- Browser
- Download file 661 Bytes
-
https://huggingface.co/Yuvrajxms09/Monad/resolve/main/config.json
- Command line
-
hf download hf://Yuvrajxms09/Monad/config.json
-
curl -L -o config.json https://huggingface.co/Yuvrajxms09/Monad/resolve/main/config.json
661 Bytes
| { | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 256, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 768, | |
| "max_position_embeddings": 2048, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 64, | |
| "num_key_value_heads": 4, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "4.57.3", | |
| "use_cache": true, | |
| "vocab_size": 8192 | |
| } | |