Instructions to use collij22/jcpsytar_FT_BioMedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use collij22/jcpsytar_FT_BioMedLM with Transformers:

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

pipe = pipeline("text-generation", model="collij22/jcpsytar_FT_BioMedLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("collij22/jcpsytar_FT_BioMedLM")
model = AutoModelForMultimodalLM.from_pretrained("collij22/jcpsytar_FT_BioMedLM")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use collij22/jcpsytar_FT_BioMedLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "collij22/jcpsytar_FT_BioMedLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "collij22/jcpsytar_FT_BioMedLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/collij22/jcpsytar_FT_BioMedLM

SGLang

How to use collij22/jcpsytar_FT_BioMedLM 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 "collij22/jcpsytar_FT_BioMedLM" \
    --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": "collij22/jcpsytar_FT_BioMedLM",
		"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 "collij22/jcpsytar_FT_BioMedLM" \
        --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": "collij22/jcpsytar_FT_BioMedLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use collij22/jcpsytar_FT_BioMedLM with Docker Model Runner:
```
docker model run hf.co/collij22/jcpsytar_FT_BioMedLM
```

collij22 commited on Mar 2, 2024

Commit

2a30d8d

verified ·

1 Parent(s): 7e09bdf

Upload tokenizer

Browse files

Files changed (2) hide show

special_tokens_map.json +1 -1
tokenizer_config.json +1 -1

special_tokens_map.json CHANGED Viewed

@@ -13,7 +13,7 @@
     "rstrip": false,
     "single_word": false
   },
-  "pad_token": "<|endoftext|>",
   "unk_token": {
     "content": "<|endoftext|>",
     "lstrip": false,

     "rstrip": false,
     "single_word": false
   },
+  "pad_token": "!",
   "unk_token": {
     "content": "<|endoftext|>",
     "lstrip": false,

tokenizer_config.json CHANGED Viewed

@@ -14,7 +14,7 @@
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|endoftext|>",
   "model_max_length": 1024,
-  "pad_token": "<|endoftext|>",
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": "<|endoftext|>"
 }

   "clean_up_tokenization_spaces": true,
   "eos_token": "<|endoftext|>",
   "model_max_length": 1024,
+  "pad_token": "!",
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": "<|endoftext|>"
 }