GGUF
Merge
mergekit
lazymergekit
bardsai/jaskier-7b-dpo-v3.3
CultriX/NeuralTrix-v4-bf16
CultriX/NeuralTrix-7B-dpo
Instructions to use CultriX/NeuralTrix-bf16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use CultriX/NeuralTrix-bf16-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="CultriX/NeuralTrix-bf16-GGUF", filename="neuraltrix-bf16.Q6_K.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 CultriX/NeuralTrix-bf16-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CultriX/NeuralTrix-bf16-GGUF:Q6_K # Run inference directly in the terminal: llama-cli -hf CultriX/NeuralTrix-bf16-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CultriX/NeuralTrix-bf16-GGUF:Q6_K # Run inference directly in the terminal: llama-cli -hf CultriX/NeuralTrix-bf16-GGUF:Q6_K
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 CultriX/NeuralTrix-bf16-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf CultriX/NeuralTrix-bf16-GGUF:Q6_K
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 CultriX/NeuralTrix-bf16-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf CultriX/NeuralTrix-bf16-GGUF:Q6_K
Use Docker
docker model run hf.co/CultriX/NeuralTrix-bf16-GGUF:Q6_K
- LM Studio
- Jan
- Ollama
How to use CultriX/NeuralTrix-bf16-GGUF with Ollama:
ollama run hf.co/CultriX/NeuralTrix-bf16-GGUF:Q6_K
- Unsloth Studio
How to use CultriX/NeuralTrix-bf16-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 CultriX/NeuralTrix-bf16-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 CultriX/NeuralTrix-bf16-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CultriX/NeuralTrix-bf16-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use CultriX/NeuralTrix-bf16-GGUF with Docker Model Runner:
docker model run hf.co/CultriX/NeuralTrix-bf16-GGUF:Q6_K
- Lemonade
How to use CultriX/NeuralTrix-bf16-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CultriX/NeuralTrix-bf16-GGUF:Q6_K
Run and chat with the model
lemonade run user.NeuralTrix-bf16-GGUF-Q6_K
List all available models
lemonade list
Note: This is a test to check if it fixed the INSTINSTINST error in the output! Please let me know if you still get errors using this model.
NeuralTrix-bf16
NeuralTrix-bf16 is a merge of the following models using LazyMergekit:
π§© Configuration
models:
- model: eren23/dpo-binarized-NeuralTrix-7B
# no parameters necessary for base model
- model: bardsai/jaskier-7b-dpo-v3.3
parameters:
density: 0.65
weight: 0.4
- model: CultriX/NeuralTrix-v4-bf16
parameters:
density: 0.6
weight: 0.35
- model: CultriX/NeuralTrix-7B-dpo
parameters:
density: 0.6
weight: 0.35
merge_method: dare_ties
base_model: eren23/dpo-binarized-NeuralTrix-7B
parameters:
int8_mask: true
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
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"])
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