Instructions to use mvasiliniuc/iva-codeint-kotlin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mvasiliniuc/iva-codeint-kotlin with Transformers:

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

pipe = pipeline("text-generation", model="mvasiliniuc/iva-codeint-kotlin")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mvasiliniuc/iva-codeint-kotlin")
model = AutoModelForCausalLM.from_pretrained("mvasiliniuc/iva-codeint-kotlin")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mvasiliniuc/iva-codeint-kotlin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mvasiliniuc/iva-codeint-kotlin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mvasiliniuc/iva-codeint-kotlin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mvasiliniuc/iva-codeint-kotlin

SGLang

How to use mvasiliniuc/iva-codeint-kotlin 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 "mvasiliniuc/iva-codeint-kotlin" \
    --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": "mvasiliniuc/iva-codeint-kotlin",
		"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 "mvasiliniuc/iva-codeint-kotlin" \
        --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": "mvasiliniuc/iva-codeint-kotlin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mvasiliniuc/iva-codeint-kotlin with Docker Model Runner:
```
docker model run hf.co/mvasiliniuc/iva-codeint-kotlin
```

mvasiliniuc commited on Jun 4, 2023

Commit

4b569bd

1 Parent(s): 3e9e68a

Upload model

Browse files

Files changed (2) hide show

config.json +4 -4
pytorch_model.bin +2 -2

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "gpt2-large",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"
@@ -12,10 +12,10 @@
   "layer_norm_epsilon": 1e-05,
   "model_type": "gpt2",
   "n_ctx": 1024,
-  "n_embd": 1280,
-  "n_head": 20,
   "n_inner": null,
-  "n_layer": 36,
   "n_positions": 1024,
   "reorder_and_upcast_attn": true,
   "resid_pdrop": 0.1,

 {
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   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"
   "layer_norm_epsilon": 1e-05,
   "model_type": "gpt2",
   "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
   "n_inner": null,
+  "n_layer": 12,
   "n_positions": 1024,
   "reorder_and_upcast_attn": true,
   "resid_pdrop": 0.1,

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