Instructions to use oplatek/falcon-7b-instruct-multi_woz_22-t2t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oplatek/falcon-7b-instruct-multi_woz_22-t2t with Transformers:

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

pipe = pipeline("text-generation", model="oplatek/falcon-7b-instruct-multi_woz_22-t2t", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("oplatek/falcon-7b-instruct-multi_woz_22-t2t", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oplatek/falcon-7b-instruct-multi_woz_22-t2t with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oplatek/falcon-7b-instruct-multi_woz_22-t2t"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oplatek/falcon-7b-instruct-multi_woz_22-t2t",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/oplatek/falcon-7b-instruct-multi_woz_22-t2t

SGLang

How to use oplatek/falcon-7b-instruct-multi_woz_22-t2t 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 "oplatek/falcon-7b-instruct-multi_woz_22-t2t" \
    --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": "oplatek/falcon-7b-instruct-multi_woz_22-t2t",
		"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 "oplatek/falcon-7b-instruct-multi_woz_22-t2t" \
        --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": "oplatek/falcon-7b-instruct-multi_woz_22-t2t",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use oplatek/falcon-7b-instruct-multi_woz_22-t2t with Docker Model Runner:
```
docker model run hf.co/oplatek/falcon-7b-instruct-multi_woz_22-t2t
```

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Check out the documentation for more information.

TRAINING LOG

wandb: Run history:
wandb: eval/loss █▆▅▄▃▃▂▂▁▁▁
wandb: eval/runtime ▁▃▂▃▃▃▃█▃▄▁
wandb: eval/samples_per_second █▆▇▆▆▆▆▁▆▄█
wandb: eval/steps_per_second █▆▇▆▆▆▆▁▆▄█
wandb: train/epoch ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb: train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb: train/learning_rate ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: train/loss █▄▄▅▃▅▃▃▄▅▃▃▃▄▃▃▃▃▂▂▂▂▃▂▄▂▃▂▂▂▂▂▃▂▁▃▂▂▂▁
wandb: train/total_flos ▁
wandb: train/train_loss ▁
wandb: train/train_runtime ▁
wandb: train/train_samples_per_second ▁
wandb: train/train_steps_per_second ▁
wandb:
wandb: Run summary:
wandb: eval/loss 0.27314
wandb: eval/runtime 129.6563
wandb: eval/samples_per_second 7.713
wandb: eval/steps_per_second 7.713
wandb: train/epoch 0.53
wandb: train/global_step 1875
wandb: train/learning_rate 0.0002
wandb: train/loss 0.258
wandb: train/total_flos 1.9547706216175334e+17
wandb: train/train_loss 0.30445
wandb: train/train_runtime 13368.3721
wandb: train/train_samples_per_second 2.244
wandb: train/train_steps_per_second 0.14
wandb:
wandb: 🚀 View run happy-deluge-17 at: https://wandb.ai/metric/llm_finetune_multiwoz22.sh/runs/4epf9h85

INFERENCE LOG

TODO

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