Instructions to use LL-Square/CodeForge-TinyLlama1.1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LL-Square/CodeForge-TinyLlama1.1B-Instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "LL-Square/CodeForge-TinyLlama1.1B-Instruct")

Transformers

How to use LL-Square/CodeForge-TinyLlama1.1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="LL-Square/CodeForge-TinyLlama1.1B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LL-Square/CodeForge-TinyLlama1.1B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LL-Square/CodeForge-TinyLlama1.1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LL-Square/CodeForge-TinyLlama1.1B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LL-Square/CodeForge-TinyLlama1.1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LL-Square/CodeForge-TinyLlama1.1B-Instruct

SGLang

How to use LL-Square/CodeForge-TinyLlama1.1B-Instruct 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 "LL-Square/CodeForge-TinyLlama1.1B-Instruct" \
    --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": "LL-Square/CodeForge-TinyLlama1.1B-Instruct",
		"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 "LL-Square/CodeForge-TinyLlama1.1B-Instruct" \
        --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": "LL-Square/CodeForge-TinyLlama1.1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LL-Square/CodeForge-TinyLlama1.1B-Instruct with Docker Model Runner:
```
docker model run hf.co/LL-Square/CodeForge-TinyLlama1.1B-Instruct
```

CodeForge-TinyLlama1.1B-Instruct / README.md

lazarusrolando

Update README.md

08a755e verified 24 days ago

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	---
	base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
	- lora
	- transformers
	license: apache-2.0
	datasets:
	- LL-Square/CodeForge-TinyLlama1.1B-Instruct
	language:
	- en
	---

	# CodeForge-Instruct

	Lightweight repository for preparing, training, and uploading small instruct-style models and LoRA adapters.

	This project contains simple scripts to train a model (`train.py`), run inference (`main.py`), configure logging (`logging_setup.py`), and upload artifacts (`upload.py`). A small sample dataset is included as `sample.jsonl`.

	## Data format

	The dataset expects newline-delimited JSON (`.jsonl`) where each line is an object with at least `prompt` and `response` (or `instruction`/`output`) fields. Example (`sample.jsonl`):

	```jsonl
	{"prompt": "Summarize the following text:", "response": "A short summary."}
	```

	Adjust `train.py` to match your field names if needed.

	## Usage

	Training (example):

	```bash
	python train.py --data sample.jsonl --output-dir ./checkpoints --epochs 3 --batch-size 8
	```

	Run inference/demo:

	```bash
	python main.py --model ./checkpoints/latest
	```

	Upload artifacts (example):

	```bash
	python upload.py --model ./checkpoints/latest --dest hub-or-bucket
	```

	See individual scripts for additional flags and configuration.

	## Logging

	The repository centralizes logging in `logging_setup.py`; import and call `setup_logging()` from other scripts to get consistent formatting and levels.

	## Development

	- Run linters/formatters as you prefer (e.g. `black`, `ruff`).
	- Add tests under a `tests/` folder if you expand behavior.

	## Contributing

	Open issues or PRs with clear reproduction steps. Keep changes minimal and scoped.

	## License

	This repository does not include a license file. Add a `LICENSE` if you plan to publish.

	### Framework versions

	- PEFT 0.18.1
	-
	---
	If you'd like, I can:
	- add a `requirements.txt` with pinned versions,
	- add CLI argument parsing examples to `train.py` and `main.py`, or
	- create a short CONTRIBUTING guide.