jarod0411/linker_v2
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How to use jarod0411/stage1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="jarod0411/stage1") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("jarod0411/stage1")
model = AutoModelForMultimodalLM.from_pretrained("jarod0411/stage1")How to use jarod0411/stage1 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jarod0411/stage1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "jarod0411/stage1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/jarod0411/stage1
How to use jarod0411/stage1 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "jarod0411/stage1" \
--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": "jarod0411/stage1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "jarod0411/stage1" \
--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": "jarod0411/stage1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use jarod0411/stage1 with Docker Model Runner:
docker model run hf.co/jarod0411/stage1
This model is a fine-tuned version of jarod0411/zinc10M_gpt2_SMILES_bpe_combined_step1 on the jarod0411/linker_v2 dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.375 | 1.0 | 23931 | 0.3615 | 0.8853 |
| 0.3609 | 2.0 | 47862 | 0.3494 | 0.8887 |
| 0.3533 | 3.0 | 71793 | 0.3432 | 0.8904 |
| 0.3486 | 4.0 | 95724 | 0.3394 | 0.8914 |
| 0.3456 | 5.0 | 119655 | 0.3367 | 0.8921 |
| 0.3432 | 6.0 | 143586 | 0.3346 | 0.8927 |
| 0.3412 | 7.0 | 167517 | 0.3333 | 0.8930 |
| 0.3397 | 8.0 | 191448 | 0.3322 | 0.8933 |
| 0.339 | 9.0 | 215379 | 0.3314 | 0.8935 |
| 0.3383 | 10.0 | 239310 | 0.3311 | 0.8936 |
Base model
openai-community/gpt2