Instructions to use KnutJaegersberg/LLongMA-3b-LIMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KnutJaegersberg/LLongMA-3b-LIMA with Transformers:

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

pipe = pipeline("text-generation", model="KnutJaegersberg/LLongMA-3b-LIMA", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/LLongMA-3b-LIMA", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("KnutJaegersberg/LLongMA-3b-LIMA", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KnutJaegersberg/LLongMA-3b-LIMA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KnutJaegersberg/LLongMA-3b-LIMA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KnutJaegersberg/LLongMA-3b-LIMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KnutJaegersberg/LLongMA-3b-LIMA

SGLang

How to use KnutJaegersberg/LLongMA-3b-LIMA 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 "KnutJaegersberg/LLongMA-3b-LIMA" \
    --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": "KnutJaegersberg/LLongMA-3b-LIMA",
		"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 "KnutJaegersberg/LLongMA-3b-LIMA" \
        --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": "KnutJaegersberg/LLongMA-3b-LIMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KnutJaegersberg/LLongMA-3b-LIMA with Docker Model Runner:
```
docker model run hf.co/KnutJaegersberg/LLongMA-3b-LIMA
```

Prompt example:

### Instruction: 
How do you fine tune a large language model? 
### Response:

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	33.66
ARC (25-shot)	39.08
HellaSwag (10-shot)	67.15
MMLU (5-shot)	26.43
TruthfulQA (0-shot)	34.71
Winogrande (5-shot)	63.38
GSM8K (5-shot)	0.3
DROP (3-shot)	4.57

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Open LLM Leaderboard Evaluation Results

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