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", device_map="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
```

lazarusrolando commited on Jun 16

Commit

08a755e

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1 Parent(s): 2199f7e

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@@ -19,30 +19,6 @@ Lightweight repository for preparing, training, and uploading small instruct-sty
 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`.
-## Quick overview
-- **Files:**
-	- `logging_setup.py` — central logging configuration used by scripts.
-	- `train.py` — training / fine-tuning entrypoint.
-	- `main.py` — minimal inference/demo runner.
-	- `upload.py` — helper to upload model artifacts to a hub or storage.
-	- `sample.jsonl` — small example dataset (one JSON object per line).
-	- `CodeForge-Instruct/` — supporting code and assets.
-## Requirements
-- Python 3.10+
-- Typical ML dependencies: `torch`, `transformers`, `peft`, `datasets`, `accelerate`, `tqdm`, `safetensors` (if used). Install example:
-```bash
-python -m venv .venv
-source .venv/Scripts/activate   # Windows: .venv\Scripts\activate
-pip install --upgrade pip
-pip install torch transformers peft datasets accelerate tqdm safetensors
-```
-If you prefer pinned dependencies, create `requirements.txt` and install via `pip install -r requirements.txt`.
 ## 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`):

 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`):