Instructions to use v000000/L3.1-Niitorm-8B-DPO-t0.0001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/L3.1-Niitorm-8B-DPO-t0.0001 with Transformers:

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

pipe = pipeline("text-generation", model="v000000/L3.1-Niitorm-8B-DPO-t0.0001")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("v000000/L3.1-Niitorm-8B-DPO-t0.0001")
model = AutoModelForCausalLM.from_pretrained("v000000/L3.1-Niitorm-8B-DPO-t0.0001")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use v000000/L3.1-Niitorm-8B-DPO-t0.0001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "v000000/L3.1-Niitorm-8B-DPO-t0.0001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "v000000/L3.1-Niitorm-8B-DPO-t0.0001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/v000000/L3.1-Niitorm-8B-DPO-t0.0001

SGLang

How to use v000000/L3.1-Niitorm-8B-DPO-t0.0001 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 "v000000/L3.1-Niitorm-8B-DPO-t0.0001" \
    --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": "v000000/L3.1-Niitorm-8B-DPO-t0.0001",
		"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 "v000000/L3.1-Niitorm-8B-DPO-t0.0001" \
        --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": "v000000/L3.1-Niitorm-8B-DPO-t0.0001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use v000000/L3.1-Niitorm-8B-DPO-t0.0001 with Docker Model Runner:
```
docker model run hf.co/v000000/L3.1-Niitorm-8B-DPO-t0.0001
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.1-Niitorm-8B-DPO

DPO Trained, Llama3.1-8B.

New: DPO'd Gutenberg Version (full epoch training).

RP model, Niitama 1.1 as a base, nearswapped with one of the smartest 3.1 models "Storm", then DPO'd, mostly abliterated.

Essentially, it's an improved Niitama 1.1

Gutenberg DPO creates more human-like prose/story writing and greately lessen synthetic feeling outputs.

llama.cpp:

thank you, mradermacher (GGUF)

thank you, QuantFactory (GGUF)

GGUF static

v0 (GGUF)

GGUF Imatrix -only q8, q6 k, q5 k s, q4 k s, iq4 x s

Finetune and merge

This is a merge and finetune of pre-trained language models.

Resultant merge finetuned on jondurbin/gutenberg-dpo-v0.1 for 1 epoch, 1.5e-5 learning rate, on Nvidia A100.

Merge Details

Merge Method

This model was merged using the NEARSWAP t0.0001 merge algorithm.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
        layer_range: [0, 32]
      - model: akjindal53244/Llama-3.1-Storm-8B
        layer_range: [0, 32]
merge_method: nearswap
base_model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
  t:
    - value: 0.0001
dtype: float16

# Then, DPO Finetune
# [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1)

DPO Notes

I used a higher learning rate and full dataset when training compared to my "L3.1-Celestial-Stone-2x8B-DPO". This caused lower loss and better adaption to the chosen style.

Prompt Template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>

Credit to Alchemonaut.

Credit to Sao10K.

Credit to Grimjim.

Credit to mlabonne.

Credit to jondurbin.

Credit to woofwolfy.