Text Generation
MLX
Safetensors
qwen3_5
unsloth
qwen
qwen3.5
reasoning
chain-of-thought
lora
competitive-programming
trace-inversion
negentropy
distillation
claude-opus-4.7
qwen3
synthetic-data
conversational
Instructions to use Jackrong/Negentropy-claude-opus-4.7-9B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Jackrong/Negentropy-claude-opus-4.7-9B-bf16 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Jackrong/Negentropy-claude-opus-4.7-9B-bf16") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Unsloth Studio new
How to use Jackrong/Negentropy-claude-opus-4.7-9B-bf16 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 Jackrong/Negentropy-claude-opus-4.7-9B-bf16 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 Jackrong/Negentropy-claude-opus-4.7-9B-bf16 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jackrong/Negentropy-claude-opus-4.7-9B-bf16 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jackrong/Negentropy-claude-opus-4.7-9B-bf16", max_seq_length=2048, ) - Pi new
How to use Jackrong/Negentropy-claude-opus-4.7-9B-bf16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Jackrong/Negentropy-claude-opus-4.7-9B-bf16"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Jackrong/Negentropy-claude-opus-4.7-9B-bf16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Jackrong/Negentropy-claude-opus-4.7-9B-bf16 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Jackrong/Negentropy-claude-opus-4.7-9B-bf16"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Jackrong/Negentropy-claude-opus-4.7-9B-bf16
Run Hermes
hermes
- MLX LM
How to use Jackrong/Negentropy-claude-opus-4.7-9B-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Jackrong/Negentropy-claude-opus-4.7-9B-bf16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Jackrong/Negentropy-claude-opus-4.7-9B-bf16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jackrong/Negentropy-claude-opus-4.7-9B-bf16", "messages": [ {"role": "user", "content": "Hello"} ] }'
Jackrong/Negentropy-claude-opus-4.7-9B-bf16
This model Jackrong/Negentropy-claude-opus-4.7-9B-bf16 was converted to MLX format from Jackrong/Negentropy-claude-opus-4.7-9B using mlx-lm version 0.30.7.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Jackrong/Negentropy-claude-opus-4.7-9B-bf16")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
- Downloads last month
- 431
Model size
9B params
Tensor type
BF16
·
Hardware compatibility
Log In to add your hardware
Quantized
Model tree for Jackrong/Negentropy-claude-opus-4.7-9B-bf16
Base model
Qwen/Qwen3.5-9B-Base Finetuned
Qwen/Qwen3.5-9B