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Quantization made by Richard Erkhov.

Orpo-Llama-3.2-1B-15k - GGUF

Model creator: https://huggingface.co/AdamLucek/
Original model: https://huggingface.co/AdamLucek/Orpo-Llama-3.2-1B-15k/

Name	Quant method	Size
Orpo-Llama-3.2-1B-15k.Q2_K.gguf	Q2_K	0.54GB
Orpo-Llama-3.2-1B-15k.Q3_K_S.gguf	Q3_K_S	0.6GB
Orpo-Llama-3.2-1B-15k.Q3_K.gguf	Q3_K	0.64GB
Orpo-Llama-3.2-1B-15k.Q3_K_M.gguf	Q3_K_M	0.64GB
Orpo-Llama-3.2-1B-15k.Q3_K_L.gguf	Q3_K_L	0.68GB
Orpo-Llama-3.2-1B-15k.IQ4_XS.gguf	IQ4_XS	0.7GB
Orpo-Llama-3.2-1B-15k.Q4_0.gguf	Q4_0	0.72GB
Orpo-Llama-3.2-1B-15k.IQ4_NL.gguf	IQ4_NL	0.72GB
Orpo-Llama-3.2-1B-15k.Q4_K_S.gguf	Q4_K_S	0.72GB
Orpo-Llama-3.2-1B-15k.Q4_K.gguf	Q4_K	0.75GB
Orpo-Llama-3.2-1B-15k.Q4_K_M.gguf	Q4_K_M	0.75GB
Orpo-Llama-3.2-1B-15k.Q4_1.gguf	Q4_1	0.77GB
Orpo-Llama-3.2-1B-15k.Q5_0.gguf	Q5_0	0.83GB
Orpo-Llama-3.2-1B-15k.Q5_K_S.gguf	Q5_K_S	0.83GB
Orpo-Llama-3.2-1B-15k.Q5_K.gguf	Q5_K	0.85GB
Orpo-Llama-3.2-1B-15k.Q5_K_M.gguf	Q5_K_M	0.85GB
Orpo-Llama-3.2-1B-15k.Q5_1.gguf	Q5_1	0.89GB
Orpo-Llama-3.2-1B-15k.Q6_K.gguf	Q6_K	0.95GB
Orpo-Llama-3.2-1B-15k.Q8_0.gguf	Q8_0	1.23GB

Original model description:

library_name: transformers license: mit datasets: - mlabonne/orpo-dpo-mix-40k base_model: - meta-llama/Llama-3.2-1B pipeline_tag: text-generation

Orpo-Llama-3.2-1B-15k

AdamLucek/Orpo-Llama-3.2-1B-15k is an ORPO fine tuned version of meta-llama/Llama-3.2-1B on a subset of 15,000 shuffled entries of mlabonne/orpo-dpo-mix-40k.

Trained for 7 hours on an L4 GPU with this training script, modified from Maxime Labonne's original guide

For full model details, refer to the base model page meta-llama/Llama-3.2-1B

Evaluations

Benchmark	Accuracy	Notes
AGIEval	20.99%	Average across multiple reasoning tasks
TruthfulQA	42.80%	MC2 accuracy
BigBench	31.75%	Average across 18 tasks
MMLU	31.23%	Average across all categories
Winogrande	61.33%	5-shot evaluation
ARC Challenge	35.92%	25-shot evaluation
HellaSwag	48.65%	10-shot evaluation

Detailed Eval Metrics Available Here

Using this Model

from transformers import AutoTokenizer
import transformers
import torch

# Load Model and Pipeline
model = "AdamLucek/Orpo-Llama-3.2-1B-15k"

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model)

# Generate Message
messages = [{"role": "user", "content": "What is a language model?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])