facebook/anli
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How to use Tverous/sft-trl-no-claim with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Tverous/sft-trl-no-claim") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Tverous/sft-trl-no-claim")
model = AutoModelForCausalLM.from_pretrained("Tverous/sft-trl-no-claim")How to use Tverous/sft-trl-no-claim with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tverous/sft-trl-no-claim"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Tverous/sft-trl-no-claim",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Tverous/sft-trl-no-claim
How to use Tverous/sft-trl-no-claim with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Tverous/sft-trl-no-claim" \
--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": "Tverous/sft-trl-no-claim",
"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 "Tverous/sft-trl-no-claim" \
--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": "Tverous/sft-trl-no-claim",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Tverous/sft-trl-no-claim with Docker Model Runner:
docker model run hf.co/Tverous/sft-trl-no-claim
This model is a fine-tuned version of EleutherAI/gpt-j-6b on the anli dataset. It achieves the following results on the evaluation set:
More information needed
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More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1604 | 0.2 | 1000 | 1.8712 |
| 1.0077 | 0.4 | 2000 | 1.4700 |
| 0.8175 | 0.6 | 3000 | 1.0772 |
| 0.7621 | 0.8 | 4000 | 0.7959 |
| 0.3346 | 1.0 | 5000 | 0.7570 |
| 0.2903 | 1.2 | 6000 | 0.6882 |
| 0.2313 | 1.4 | 7000 | 0.6471 |
| 0.3038 | 1.6 | 8000 | 0.6203 |
| 0.2292 | 1.8 | 9000 | 0.6203 |
| 0.193 | 2.0 | 10000 | 0.6203 |
| 0.1198 | 2.2 | 11000 | 0.6203 |
| 0.2185 | 2.4 | 12000 | 0.6203 |
| 0.1038 | 2.6 | 13000 | 0.6203 |
| 0.1469 | 2.8 | 14000 | 0.6203 |
| 0.1857 | 3.0 | 15000 | 0.6203 |