Instructions to use modrill/opencodeinst_5k_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/opencodeinst_5k_sft with Transformers:

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

pipe = pipeline("text-generation", model="modrill/opencodeinst_5k_sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("modrill/opencodeinst_5k_sft", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use modrill/opencodeinst_5k_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/opencodeinst_5k_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modrill/opencodeinst_5k_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/opencodeinst_5k_sft

SGLang

How to use modrill/opencodeinst_5k_sft 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 "modrill/opencodeinst_5k_sft" \
    --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": "modrill/opencodeinst_5k_sft",
		"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 "modrill/opencodeinst_5k_sft" \
        --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": "modrill/opencodeinst_5k_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/opencodeinst_5k_sft with Docker Model Runner:
```
docker model run hf.co/modrill/opencodeinst_5k_sft
```

modrill commited on 11 days ago

Commit

9d46019

verified ·

1 Parent(s): f10081c

Update model card

Browse files

Files changed (1) hide show

README.md +8 -54

README.md CHANGED Viewed

@@ -1,61 +1,15 @@
 ---
-library_name: peft
-license: other
-base_model: google/gemma-3-1b-it
 tags:
-- base_model:adapter:google/gemma-3-1b-it
-- llama-factory
-- lora
-- transformers
-pipeline_tag: text-generation
-model-index:
-- name: opencodeinst_5k_sft
-  results: []
 ---
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 # opencodeinst_5k_sft
-This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the opencodeinst_5k dataset.
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 1e-06
-- train_batch_size: 1
-- eval_batch_size: 4
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 8
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 0.1
-- num_epochs: 3.0
-### Training results
-### Framework versions
-- PEFT 0.18.1
-- Transformers 5.2.0
-- Pytorch 2.11.0+cu130
-- Datasets 4.0.0
-- Tokenizers 0.22.2

 ---
+license: cc-by-nc-4.0
 tags:
+- mhm
+- text-generation
+library_name: transformers
 ---
 # opencodeinst_5k_sft
+Auto-uploaded by watcher (MergeBench excluded).
+- Source path: `LlamaFactory/models/opencodeinst_5k_sft`
+- Uploaded at: `2026-05-20T06:04:34.897329`
+- Visibility: `public`