Instructions to use mamiksik/CodeBertaCLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mamiksik/CodeBertaCLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mamiksik/CodeBertaCLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mamiksik/CodeBertaCLM") model = AutoModelForCausalLM.from_pretrained("mamiksik/CodeBertaCLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mamiksik/CodeBertaCLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mamiksik/CodeBertaCLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mamiksik/CodeBertaCLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mamiksik/CodeBertaCLM
- SGLang
How to use mamiksik/CodeBertaCLM 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 "mamiksik/CodeBertaCLM" \ --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": "mamiksik/CodeBertaCLM", "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 "mamiksik/CodeBertaCLM" \ --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": "mamiksik/CodeBertaCLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mamiksik/CodeBertaCLM with Docker Model Runner:
docker model run hf.co/mamiksik/CodeBertaCLM
End of training (Seq2SeqTraining) golden-water-7
Browse files- config.json +1 -0
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
config.json
CHANGED
|
@@ -12,6 +12,7 @@
|
|
| 12 |
"hidden_size": 768,
|
| 13 |
"initializer_range": 0.02,
|
| 14 |
"intermediate_size": 3072,
|
|
|
|
| 15 |
"layer_norm_eps": 1e-05,
|
| 16 |
"max_position_embeddings": 514,
|
| 17 |
"model_type": "roberta",
|
|
|
|
| 12 |
"hidden_size": 768,
|
| 13 |
"initializer_range": 0.02,
|
| 14 |
"intermediate_size": 3072,
|
| 15 |
+
"is_decoder": true,
|
| 16 |
"layer_norm_eps": 1e-05,
|
| 17 |
"max_position_embeddings": 514,
|
| 18 |
"model_type": "roberta",
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 498875705
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4b135410d104c9e085d41bc23458966a7ebaff5c832488656e08a8f658eef3cb
|
| 3 |
size 498875705
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3515
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c1437b5c5a1ced301c0ac2ecc62ff64307c6a04338d0e9414531798a9cdd0da6
|
| 3 |
size 3515
|