Instructions to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf",
	filename="dpo-binarized-NeuralTrix-7B.IQ3_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/eren23_-_dpo-binarized-NeuralTrix-7B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.eren23_-_dpo-binarized-NeuralTrix-7B-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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dpo-binarized-NeuralTrix-7B - GGUF

Model creator: https://huggingface.co/eren23/
Original model: https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B/

Name	Quant method	Size
dpo-binarized-NeuralTrix-7B.Q2_K.gguf	Q2_K	2.53GB
dpo-binarized-NeuralTrix-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
dpo-binarized-NeuralTrix-7B.IQ3_S.gguf	IQ3_S	2.96GB
dpo-binarized-NeuralTrix-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
dpo-binarized-NeuralTrix-7B.IQ3_M.gguf	IQ3_M	3.06GB
dpo-binarized-NeuralTrix-7B.Q3_K.gguf	Q3_K	3.28GB
dpo-binarized-NeuralTrix-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
dpo-binarized-NeuralTrix-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
dpo-binarized-NeuralTrix-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
dpo-binarized-NeuralTrix-7B.Q4_0.gguf	Q4_0	3.83GB
dpo-binarized-NeuralTrix-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
dpo-binarized-NeuralTrix-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
dpo-binarized-NeuralTrix-7B.Q4_K.gguf	Q4_K	4.07GB
dpo-binarized-NeuralTrix-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
dpo-binarized-NeuralTrix-7B.Q4_1.gguf	Q4_1	4.24GB
dpo-binarized-NeuralTrix-7B.Q5_0.gguf	Q5_0	4.65GB
dpo-binarized-NeuralTrix-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
dpo-binarized-NeuralTrix-7B.Q5_K.gguf	Q5_K	4.78GB
dpo-binarized-NeuralTrix-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
dpo-binarized-NeuralTrix-7B.Q5_1.gguf	Q5_1	5.07GB
dpo-binarized-NeuralTrix-7B.Q6_K.gguf	Q6_K	5.53GB
dpo-binarized-NeuralTrix-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language: - en license: apache-2.0 tags: - conversation - text-generation-inference - CultriX/NeuralTrix-7B-dpo - dpo - merge datasets: - argilla/OpenHermes2.5-dpo-binarized-alpha pipeline_tag: text-generation model-index: - name: dpo-binarized-NeuralTrix-7B 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: 72.35 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/dpo-binarized-NeuralTrix-7B 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: 88.89 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/dpo-binarized-NeuralTrix-7B 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: 64.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/dpo-binarized-NeuralTrix-7B 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: 79.07 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/dpo-binarized-NeuralTrix-7B 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: 84.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/dpo-binarized-NeuralTrix-7B 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: 68.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/dpo-binarized-NeuralTrix-7B name: Open LLM Leaderboard

DPO Finetuned CultriX/NeuralTrix-7B-dpo using argilla/OpenHermes2.5-dpo-binarized-alpha

argilla dpo binarized pairs is a dataset built on top of: https://huggingface.co/datasets/teknium/OpenHermes-2.5 using https://github.com/argilla-io/distilabel if interested.

Thx for the great data sources.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	76.17
AI2 Reasoning Challenge (25-Shot)	72.35
HellaSwag (10-Shot)	88.89
MMLU (5-Shot)	64.09
TruthfulQA (0-shot)	79.07
Winogrande (5-shot)	84.61
GSM8k (5-shot)	68.01

Downloads last month: 10

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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