Instructions to use long2811/writing_task2_feedback_with_score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use long2811/writing_task2_feedback_with_score with Transformers:

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

pipe = pipeline("text-generation", model="long2811/writing_task2_feedback_with_score")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("long2811/writing_task2_feedback_with_score")
model = AutoModelForCausalLM.from_pretrained("long2811/writing_task2_feedback_with_score")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use long2811/writing_task2_feedback_with_score with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "long2811/writing_task2_feedback_with_score"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "long2811/writing_task2_feedback_with_score",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/long2811/writing_task2_feedback_with_score

SGLang

How to use long2811/writing_task2_feedback_with_score 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 "long2811/writing_task2_feedback_with_score" \
    --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": "long2811/writing_task2_feedback_with_score",
		"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 "long2811/writing_task2_feedback_with_score" \
        --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": "long2811/writing_task2_feedback_with_score",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use long2811/writing_task2_feedback_with_score with Docker Model Runner:
```
docker model run hf.co/long2811/writing_task2_feedback_with_score
```

long2811 commited on Dec 31, 2024

Commit

23519b2

verified ·

1 Parent(s): f3081d6

Update tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +6 -1

tokenizer_config.json CHANGED Viewed

@@ -35,5 +35,10 @@
   "model_max_length": 1000000000000000019884624838656,
   "tokenizer_class": "LlamaTokenizer",
   "unk_token": "<unk>",
-  "use_default_system_prompt": false
 }

   "model_max_length": 1000000000000000019884624838656,
   "tokenizer_class": "LlamaTokenizer",
   "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": {
+    "system": "<|start_header_id|>system<|end_header_id|>",
+    "user": "<|start_header_id|>user<|end_header_id|>",
+    "assistant": "<|start_header_id|>assistant<|end_header_id|>"
+  }
 }