Instructions to use frhew/sigdial_ft_a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use frhew/sigdial_ft_a2 with Transformers:

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

pipe = pipeline("text-generation", model="frhew/sigdial_ft_a2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("frhew/sigdial_ft_a2")
model = AutoModelForCausalLM.from_pretrained("frhew/sigdial_ft_a2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use frhew/sigdial_ft_a2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "frhew/sigdial_ft_a2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "frhew/sigdial_ft_a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/frhew/sigdial_ft_a2

SGLang

How to use frhew/sigdial_ft_a2 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 "frhew/sigdial_ft_a2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "frhew/sigdial_ft_a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "frhew/sigdial_ft_a2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "frhew/sigdial_ft_a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use frhew/sigdial_ft_a2 with Docker Model Runner:
```
docker model run hf.co/frhew/sigdial_ft_a2
```

frhew commited on Nov 13, 2024

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@@ -112,7 +112,7 @@ A sample of the data used to train our model can be found [here](https://github.
 ## Evaluation
-The right hand side shows the results of the manual evaluation, done on the outputs from each model for 35 texts. M.P. stands for meaning preservation, S for simplification, C for coherence, F for factuality; the score represents the percentage of `yes' answers.
 More details on the evaluation can be found in the paper. For all metrics, higher is better.
 | **Model** | **Prompt** | **Test set** | **SARI** | **FRE** | **M.P.** | **S** | **C** | **F** | **Avg.** |
@@ -126,7 +126,7 @@ More details on the evaluation can be found in the paper. For all metrics, highe
 #### Summary
-## Citation [optional]
 **BibTeX:**

 ## Evaluation
+The right hand side shows the results of the manual evaluation, done on the outputs from each model for 35 texts. M.P. stands for meaning preservation, S for simplification, C for coherence, F for factuality; the score represents the percentage of *yes* answers.
 More details on the evaluation can be found in the paper. For all metrics, higher is better.
 | **Model** | **Prompt** | **Test set** | **SARI** | **FRE** | **M.P.** | **S** | **C** | **F** | **Avg.** |
 #### Summary
+## Citation
 **BibTeX:**