Instructions to use himalaya-ai/himalayagpt-0.5b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himalaya-ai/himalayagpt-0.5b-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="himalaya-ai/himalayagpt-0.5b-it", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("himalaya-ai/himalayagpt-0.5b-it", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("himalaya-ai/himalayagpt-0.5b-it", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use himalaya-ai/himalayagpt-0.5b-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "himalaya-ai/himalayagpt-0.5b-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "himalaya-ai/himalayagpt-0.5b-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/himalaya-ai/himalayagpt-0.5b-it
- SGLang
How to use himalaya-ai/himalayagpt-0.5b-it 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 "himalaya-ai/himalayagpt-0.5b-it" \ --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": "himalaya-ai/himalayagpt-0.5b-it", "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 "himalaya-ai/himalayagpt-0.5b-it" \ --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": "himalaya-ai/himalayagpt-0.5b-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use himalaya-ai/himalayagpt-0.5b-it with Docker Model Runner:
docker model run hf.co/himalaya-ai/himalayagpt-0.5b-it
| library_name: transformers | |
| tags: | |
| - nanochat | |
| - causal-lm | |
| - trust-remote-code | |
| # local/nanochat-export | |
| Exported from nanochat checkpoints with custom `transformers` remote code. | |
| ## Load | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| repo = "himalaya-ai/himalayagpt-0.5b-it" | |
| tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| repo, | |
| trust_remote_code=True, | |
| torch_dtype="auto", | |
| device_map="auto" if torch.cuda.is_available() else None, | |
| ) | |
| prompt = "नेपालको राजधानी " | |
| ids = tok(prompt, return_tensors="pt").input_ids.to(model.device) | |
| out = model.generate(ids, max_new_tokens=64) | |
| print(tok.decode(out[0], skip_special_tokens=True)) | |
| ``` | |
| ## Notes | |
| - This repo uses custom model/tokenizer code (`trust_remote_code=True`). | |
| - Checkpoint source: `sft` | |
| - Model tag: `d15_harl_fulltokens_sdpa_bs32` | |
| - Step: `49164` | |