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

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08a755e verified 24 days ago

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metadata

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):

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

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

Usage

Training (example):

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

Run inference/demo:

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

Upload artifacts (example):

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.