Instructions to use vedanta2003/ipd_finetuned2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vedanta2003/ipd_finetuned2 with Transformers:

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

pipe = pipeline("text-generation", model="vedanta2003/ipd_finetuned2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vedanta2003/ipd_finetuned2")
model = AutoModelForCausalLM.from_pretrained("vedanta2003/ipd_finetuned2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vedanta2003/ipd_finetuned2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vedanta2003/ipd_finetuned2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vedanta2003/ipd_finetuned2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vedanta2003/ipd_finetuned2

SGLang

How to use vedanta2003/ipd_finetuned2 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 "vedanta2003/ipd_finetuned2" \
    --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": "vedanta2003/ipd_finetuned2",
		"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 "vedanta2003/ipd_finetuned2" \
        --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": "vedanta2003/ipd_finetuned2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vedanta2003/ipd_finetuned2 with Docker Model Runner:
```
docker model run hf.co/vedanta2003/ipd_finetuned2
```

ipd_finetuned2 / config.json

vedanta2003

Upload RobertaForCausalLM

cc8b2d4 verified over 2 years ago

Raw History Blame Contribute Delete

901 Bytes

	{
	"_name_or_path": "cardiffnlp/twitter-roberta-base-sentiment-latest",
	"architectures": [
	"RobertaForCausalLM"
	],
	"attention_probs_dropout_prob": 0.1,
	"bos_token_id": 0,
	"classifier_dropout": null,
	"eos_token_id": 2,
	"gradient_checkpointing": false,
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.1,
	"hidden_size": 768,
	"id2label": {
	"0": "negative",
	"1": "neutral",
	"2": "positive"
	},
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"label2id": {
	"negative": 0,
	"neutral": 1,
	"positive": 2
	},
	"layer_norm_eps": 1e-05,
	"max_position_embeddings": 514,
	"model_type": "roberta",
	"num_attention_heads": 12,
	"num_hidden_layers": 12,
	"pad_token_id": 1,
	"position_embedding_type": "absolute",
	"torch_dtype": "float16",
	"transformers_version": "4.31.0",
	"type_vocab_size": 1,
	"use_cache": true,
	"vocab_size": 50265
	}