Text Generation
Transformers
Safetensors
arctic
snowflake
Mixture of Experts
conversational
custom_code
Instructions to use Snowflake/snowflake-arctic-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Snowflake/snowflake-arctic-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Snowflake/snowflake-arctic-instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Snowflake/snowflake-arctic-instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Snowflake/snowflake-arctic-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Snowflake/snowflake-arctic-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Snowflake/snowflake-arctic-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Snowflake/snowflake-arctic-instruct
- SGLang
How to use Snowflake/snowflake-arctic-instruct 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 "Snowflake/snowflake-arctic-instruct" \ --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": "Snowflake/snowflake-arctic-instruct", "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 "Snowflake/snowflake-arctic-instruct" \ --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": "Snowflake/snowflake-arctic-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Snowflake/snowflake-arctic-instruct with Docker Model Runner:
docker model run hf.co/Snowflake/snowflake-arctic-instruct
Download config.json from Snowflake/snowflake-arctic-instruct: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/Snowflake/snowflake-arctic-instruct/resolve/d9a231a8e6a1d4690c96d2762db3181895cb94c2/config.json
- Command line
-
hf download hf://Snowflake/snowflake-arctic-instruct@d9a231a8e6a1d4690c96d2762db3181895cb94c2/config.json
-
curl -L -o config.json https://huggingface.co/Snowflake/snowflake-arctic-instruct/resolve/d9a231a8e6a1d4690c96d2762db3181895cb94c2/config.json
1.27 kB
| { | |
| "architectures": [ | |
| "YakForCausalLM" | |
| ], | |
| "attention_dropout": 0, | |
| "bos_token_id": 1, | |
| "enable_expert_tensor_parallelism": false, | |
| "enc_index": [ | |
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| ], | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 7168, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4864, | |
| "max_position_embeddings": 4096, | |
| "max_sequence_length": 4096, | |
| "model_type": "yak", | |
| "moe_eval_capacity_factor": 1, | |
| "moe_layer_frequency": 1, | |
| "moe_min_capacity": 0, | |
| "moe_token_dropping": false, | |
| "moe_train_capacity_factor": 1, | |
| "num_attention_heads": 56, | |
| "num_experts_per_tok": 2, | |
| "num_hidden_layers": 35, | |
| "num_key_value_heads": 56, | |
| "num_local_experts": 128, | |
| "parallel_attn_mlp_res": true, | |
| "quantization": null, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta": 10000, | |
| "router_aux_loss_coef": 0.001, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.39.0.dev0", | |
| "use_cache": true, | |
| "use_residual": true, | |
| "vocab_size": 32000 | |
| } |