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

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"}
     ]
   }'

Negentropy-claude-opus-4.7-9B-bf16 / README.md

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metadata

tags:
  - unsloth
  - qwen
  - qwen3.5
  - reasoning
  - chain-of-thought
  - lora
  - competitive-programming
  - trace-inversion
  - negentropy
  - distillation
  - claude-opus-4.7
  - qwen3
  - synthetic-data
  - mlx
license: apache-2.0
language:
  - en
  - zh
  - ko
  - ja
  - ru
base_model: Jackrong/Negentropy-claude-opus-4.7-9B
pipeline_tag: text-generation
library_name: mlx

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)