Instructions to use RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits

SGLang

How to use RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits 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 "RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits" \
    --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": "RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits",
		"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 "RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits" \
        --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": "RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/eren23_-_DistiLabelOrca-TinyLLama-1.1B-4bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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DistiLabelOrca-TinyLLama-1.1B - bnb 4bits

Model creator: https://huggingface.co/eren23/
Original model: https://huggingface.co/eren23/DistiLabelOrca-TinyLLama-1.1B/

Original model description:

language: - en license: apache-2.0 library_name: transformers datasets: - argilla/distilabel-intel-orca-dpo-pairs pipeline_tag: question-answering model-index: - name: DistiLabelOrca-TinyLLama-1.1B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 36.18 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistiLabelOrca-TinyLLama-1.1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 61.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistiLabelOrca-TinyLLama-1.1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistiLabelOrca-TinyLLama-1.1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 38.05 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistiLabelOrca-TinyLLama-1.1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 60.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistiLabelOrca-TinyLLama-1.1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.67 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistiLabelOrca-TinyLLama-1.1B name: Open LLM Leaderboard

TinyLlama/TinyLlama-1.1B-Chat-v1.0 dpo finetuned on the argilla/distilabel-intel-orca-dpo-pairs dataset, which is the distilled version of https://huggingface.co/datasets/Intel/orca_dpo_pairs

GGUF Version: To be added Exllama Version: To be added

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	37.17
AI2 Reasoning Challenge (25-Shot)	36.18
HellaSwag (10-Shot)	61.15
MMLU (5-Shot)	25.09
TruthfulQA (0-shot)	38.05
Winogrande (5-shot)	60.85
GSM8k (5-shot)	1.67

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Safetensors

Model size

1B params

Tensor type

F32

F16