Instructions to use amphora/small-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amphora/small-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="amphora/small-instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("amphora/small-instruct") model = AutoModelForCausalLM.from_pretrained("amphora/small-instruct") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use amphora/small-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "amphora/small-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amphora/small-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/amphora/small-instruct
- SGLang
How to use amphora/small-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 "amphora/small-instruct" \ --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": "amphora/small-instruct", "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 "amphora/small-instruct" \ --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": "amphora/small-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use amphora/small-instruct with Docker Model Runner:
docker model run hf.co/amphora/small-instruct
Upload LlamaForCausalLM
Browse files- config.json +29 -0
- generation_config.json +8 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "./lora-out",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"bos_token_id": 50257,
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"eos_token_id": 50256,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.01,
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"intermediate_size": 5632,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 32,
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"pad_token_id": 50258,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"tokenizer_class": "GPT2TokenizerFast",
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"torch_dtype": "bfloat16",
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"transformers_version": "4.35.0.dev0",
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"use_cache": false,
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"vocab_size": 50304
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50257,
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"eos_token_id": 50256,
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"pad_token_id": 50258,
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"transformers_version": "4.35.0.dev0",
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"use_cache": false
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8059fabaeec6c9e4c719bbc0847356837499fd48163be7d6026af59f27ca29a
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size 2878615417
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