Instructions to use DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2 with Transformers:

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

pipe = pipeline("text-generation", model="DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2

SGLang

How to use DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2 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 "DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2" \
    --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": "DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2",
		"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 "DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2" \
        --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": "DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2 with Docker Model Runner:
```
docker model run hf.co/DevQuasar/vintage-nextstep_os_systemadmin-ft-phi2
```

csabakecskemeti commited on Mar 13, 2024

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@@ -16,7 +16,7 @@ contains less than 100 tokens). The maximum token size for Orca2 is 4096 so a si
 Evaluation set has been generated similar method on 1% of the raw data with LLama2 chat (https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF).
 Trained locally on 2x3090 GPU with vanila DDP with HuggingFace Accelerate for 50 Epoch.
-As I wanted to add new knowledge to the base model r=128 and lora_alpha=128 has been used -> LoRA weights are 3.5% of the base model.
 Chat with model sample code:

 Evaluation set has been generated similar method on 1% of the raw data with LLama2 chat (https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF).
 Trained locally on 2x3090 GPU with vanila DDP with HuggingFace Accelerate for 50 Epoch.
+As I wanted to add new knowledge to the base model r=128 and lora_alpha=128 has been used -> LoRA weights were 3.5% of the base model.
 Chat with model sample code: