Instructions to use Cesar42/TrainLlama2Dataset3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cesar42/TrainLlama2Dataset3 with Transformers:

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

pipe = pipeline("text-generation", model="Cesar42/TrainLlama2Dataset3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cesar42/TrainLlama2Dataset3")
model = AutoModelForCausalLM.from_pretrained("Cesar42/TrainLlama2Dataset3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Cesar42/TrainLlama2Dataset3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cesar42/TrainLlama2Dataset3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cesar42/TrainLlama2Dataset3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Cesar42/TrainLlama2Dataset3

SGLang

How to use Cesar42/TrainLlama2Dataset3 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 "Cesar42/TrainLlama2Dataset3" \
    --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": "Cesar42/TrainLlama2Dataset3",
		"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 "Cesar42/TrainLlama2Dataset3" \
        --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": "Cesar42/TrainLlama2Dataset3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Cesar42/TrainLlama2Dataset3 with Docker Model Runner:
```
docker model run hf.co/Cesar42/TrainLlama2Dataset3
```

Cesar42 commited on Oct 15, 2023

Commit

e21a1f5

1 Parent(s): 5e4280b

Create config.json

Browse files

Files changed (1) hide show

config.json +1 -0

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+ { "_name_or_path": "NousResearch/Llama-2-7b-chat-hf", "architectures": [ "LlamaForCausalLM" ], "attention_bias": false, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 11008, "max_position_embeddings": 4096, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 32, "pad_token_id": 0, "pretraining_tp": 1, "quantization_config": { "bnb_4bit_compute_dtype": "float16", "bnb_4bit_quant_type": "nf4", "bnb_4bit_use_double_quant": false, "llm_int8_enable_fp32_cpu_offload": false, "llm_int8_has_fp16_weight": false, "llm_int8_skip_modules": null, "llm_int8_threshold": 6.0, "load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes" }, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 10000.0, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.34.0", "use_cache": false, "vocab_size": 32001 }