Instructions to use Connorvr/BrightBot-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Connorvr/BrightBot-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Connorvr/BrightBot-small") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Connorvr/BrightBot-small") model = AutoModelForCausalLM.from_pretrained("Connorvr/BrightBot-small") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Connorvr/BrightBot-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Connorvr/BrightBot-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Connorvr/BrightBot-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Connorvr/BrightBot-small
- SGLang
How to use Connorvr/BrightBot-small 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 "Connorvr/BrightBot-small" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Connorvr/BrightBot-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Connorvr/BrightBot-small" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Connorvr/BrightBot-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Connorvr/BrightBot-small with Docker Model Runner:
docker model run hf.co/Connorvr/BrightBot-small
Retrain on gender neutral data
Browse files- eval_results.txt +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
- training_data.csv +0 -0
eval_results.txt
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
perplexity = tensor(1.
|
|
|
|
| 1 |
+
perplexity = tensor(1.6352)
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 510403817
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:acead3775bcc80b15f9d9a0c2af7e17109c87daeab341ef07a252d406c5c25ff
|
| 3 |
size 510403817
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1327
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9cc944ea6b2746fb620c1201cb14a3cec086bfeccd68159e9fb6306794692660
|
| 3 |
size 1327
|
training_data.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|