Instructions to use mole-code/continue-java-lib-codegen-2B-mono-fft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mole-code/continue-java-lib-codegen-2B-mono-fft with Transformers:

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

pipe = pipeline("text-generation", model="mole-code/continue-java-lib-codegen-2B-mono-fft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mole-code/continue-java-lib-codegen-2B-mono-fft")
model = AutoModelForCausalLM.from_pretrained("mole-code/continue-java-lib-codegen-2B-mono-fft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mole-code/continue-java-lib-codegen-2B-mono-fft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mole-code/continue-java-lib-codegen-2B-mono-fft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mole-code/continue-java-lib-codegen-2B-mono-fft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mole-code/continue-java-lib-codegen-2B-mono-fft

SGLang

How to use mole-code/continue-java-lib-codegen-2B-mono-fft 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 "mole-code/continue-java-lib-codegen-2B-mono-fft" \
    --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": "mole-code/continue-java-lib-codegen-2B-mono-fft",
		"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 "mole-code/continue-java-lib-codegen-2B-mono-fft" \
        --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": "mole-code/continue-java-lib-codegen-2B-mono-fft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mole-code/continue-java-lib-codegen-2B-mono-fft with Docker Model Runner:
```
docker model run hf.co/mole-code/continue-java-lib-codegen-2B-mono-fft
```

continue-java-lib-codegen-2B-mono-fft

File size: 1,004 Bytes

0da35de

{
  "_name_or_path": "Salesforce/codegen-2B-mono",
  "activation_function": "gelu_new",
  "architectures": [
    "CodeGenForCausalLM"
  ],
  "attn_pdrop": 0.0,
  "bos_token_id": 1,
  "embd_pdrop": 0.0,
  "eos_token_id": 50256,
  "gradient_checkpointing": false,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "codegen",
  "n_ctx": 2048,
  "n_embd": 2560,
  "n_head": 32,
  "n_inner": null,
  "n_layer": 32,
  "n_positions": 2048,
  "resid_pdrop": 0.0,
  "rotary_dim": 64,
  "scale_attn_weights": true,
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "task_specific_params": {
    "text-generation": {
      "do_sample": true,
      "max_length": 50,
      "temperature": 1.0
    }
  },
  "tie_word_embeddings": false,
  "tokenizer_class": "GPT2Tokenizer",
  "torch_dtype": "float32",
  "transformers_version": "4.38.2",
  "use_cache": false,
  "vocab_size": 51200
}