Instructions to use routellm/causal_llm_gpt4_augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use routellm/causal_llm_gpt4_augmented with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="routellm/causal_llm_gpt4_augmented")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("routellm/causal_llm_gpt4_augmented")
model = AutoModelForCausalLM.from_pretrained("routellm/causal_llm_gpt4_augmented", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use routellm/causal_llm_gpt4_augmented with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "routellm/causal_llm_gpt4_augmented"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "routellm/causal_llm_gpt4_augmented",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/routellm/causal_llm_gpt4_augmented

SGLang

How to use routellm/causal_llm_gpt4_augmented 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 "routellm/causal_llm_gpt4_augmented" \
    --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": "routellm/causal_llm_gpt4_augmented",
		"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 "routellm/causal_llm_gpt4_augmented" \
        --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": "routellm/causal_llm_gpt4_augmented",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use routellm/causal_llm_gpt4_augmented with Docker Model Runner:
```
docker model run hf.co/routellm/causal_llm_gpt4_augmented
```

causal_llm_gpt4_augmented / config.json

iojw

Add augmented causal LLM router

67b336c over 2 years ago

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{"_name_or_path": "meta-llama/Meta-Llama-3-8B", "architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128001, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.37.0.dev0", "use_cache": false, "vocab_size": 128261}