Instructions to use chillymiao/Hyacinth6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chillymiao/Hyacinth6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chillymiao/Hyacinth6B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chillymiao/Hyacinth6B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use chillymiao/Hyacinth6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chillymiao/Hyacinth6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chillymiao/Hyacinth6B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/chillymiao/Hyacinth6B
- SGLang
How to use chillymiao/Hyacinth6B 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 "chillymiao/Hyacinth6B" \ --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": "chillymiao/Hyacinth6B", "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 "chillymiao/Hyacinth6B" \ --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": "chillymiao/Hyacinth6B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use chillymiao/Hyacinth6B with Docker Model Runner:
docker model run hf.co/chillymiao/Hyacinth6B
| { | |
| "_name_or_path": "Hyacinth6B", | |
| "add_bias_linear": false, | |
| "add_qkv_bias": true, | |
| "apply_query_key_layer_scaling": true, | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "ChatGLMModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_softmax_in_fp32": true, | |
| "auto_map": { | |
| "AutoConfig": "configuration_chatglm.ChatGLMConfig", | |
| "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration", | |
| "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration", | |
| "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration", | |
| "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification" | |
| }, | |
| "bias_dropout_fusion": true, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "ffn_hidden_size": 13696, | |
| "fp32_residual_connection": false, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 4096, | |
| "kv_channels": 128, | |
| "layernorm_epsilon": 1e-05, | |
| "model_type": "chatglm", | |
| "multi_query_attention": true, | |
| "multi_query_group_num": 2, | |
| "num_attention_heads": 32, | |
| "num_layers": 28, | |
| "original_rope": true, | |
| "pad_token_id": 0, | |
| "padded_vocab_size": 65024, | |
| "post_layer_norm": true, | |
| "pre_seq_len": null, | |
| "prefix_projection": false, | |
| "quantization_bit": 0, | |
| "rmsnorm": true, | |
| "seq_length": 32768, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.31.0", | |
| "use_cache": true, | |
| "vocab_size": 65024 | |
| } | |