Instructions to use mtzig/lltransformer-fwe-test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mtzig/lltransformer-fwe-test2 with Transformers:

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

pipe = pipeline("text-generation", model="mtzig/lltransformer-fwe-test2")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mtzig/lltransformer-fwe-test2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mtzig/lltransformer-fwe-test2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mtzig/lltransformer-fwe-test2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mtzig/lltransformer-fwe-test2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mtzig/lltransformer-fwe-test2

SGLang

How to use mtzig/lltransformer-fwe-test2 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 "mtzig/lltransformer-fwe-test2" \
    --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": "mtzig/lltransformer-fwe-test2",
		"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 "mtzig/lltransformer-fwe-test2" \
        --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": "mtzig/lltransformer-fwe-test2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mtzig/lltransformer-fwe-test2 with Docker Model Runner:
```
docker model run hf.co/mtzig/lltransformer-fwe-test2
```

lltransformer-fwe-test2 / train_results.json

mtzig

End of training: push final checkpoint

48145ea verified about 1 year ago

Raw

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479 Bytes

	{
	"epoch": 0.9922480620155039,
	"eval_epoch": 0.9922480620155039,
	"eval_eval_loss": 10.453876495361328,
	"eval_eval_runtime": 0.4463,
	"eval_eval_samples_per_second": 11.203,
	"eval_eval_steps_per_second": 4.481,
	"eval_perplexity": 34678.54570620386,
	"total_flos": 384752733388800.0,
	"train_loss": 86.94341468811035,
	"train_runtime": 61.6055,
	"train_samples": 515,
	"train_samples_per_second": 8.36,
	"train_steps_per_second": 0.26
	}