Text Generation
Transformers
Safetensors
English
microloop_diffusion
causal-lm
base-model
small-language-model
custom_code
muon
hummingbird-v1
conversational
Instructions to use juinron/Hummingbird-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juinron/Hummingbird-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="juinron/Hummingbird-V1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("juinron/Hummingbird-V1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use juinron/Hummingbird-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "juinron/Hummingbird-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "juinron/Hummingbird-V1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/juinron/Hummingbird-V1
- SGLang
How to use juinron/Hummingbird-V1 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 "juinron/Hummingbird-V1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "juinron/Hummingbird-V1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "juinron/Hummingbird-V1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "juinron/Hummingbird-V1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use juinron/Hummingbird-V1 with Docker Model Runner:
docker model run hf.co/juinron/Hummingbird-V1
| { | |
| "document_end_id": 8, | |
| "elapsed_seconds": 541.1, | |
| "format_version": 3, | |
| "held_out_percent": 1, | |
| "mix_policy": "token_balanced", | |
| "per_source_tokens_all_splits": { | |
| "natural_cosmopedia_v2": 168663969, | |
| "natural_dclm": 211970663, | |
| "natural_finemath_4plus": 53640449, | |
| "natural_fineweb_edu": 570339212, | |
| "natural_fineweb_hq": 109226534 | |
| }, | |
| "row_targets": { | |
| "broad_web": 0.2, | |
| "educational_web": 0.5, | |
| "high_quality_web": 0.1, | |
| "mathematics": 0.05, | |
| "textbook_exposition": 0.15 | |
| }, | |
| "row_tokens_all_splits": { | |
| "broad_web": 211970663, | |
| "educational_web": 570339212, | |
| "high_quality_web": 109226534, | |
| "mathematics": 53640449, | |
| "textbook_exposition": 168663969 | |
| }, | |
| "splits": { | |
| "held_out": { | |
| "document_ends_file": "held_out_document_ends.npy", | |
| "documents": 8586, | |
| "token_file": "held_out_tokens.int32", | |
| "tokens": 11432331 | |
| }, | |
| "train": { | |
| "document_ends_file": "train_document_ends.npy", | |
| "documents": 824997, | |
| "token_file": "train_tokens.int32", | |
| "tokens": 1091660174 | |
| }, | |
| "validation": { | |
| "document_ends_file": "validation_document_ends.npy", | |
| "documents": 8437, | |
| "token_file": "validation_tokens.int32", | |
| "tokens": 10748322 | |
| } | |
| }, | |
| "tokenizer_path": "D:\\llm\\frost\\artifacts\\runs\\e3_tokenizers\\tok_4k_digit", | |
| "validation_percent": 1 | |
| } | |