Instructions to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx") model = AutoModelForCausalLM.from_pretrained("nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx") 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]:])) - MLX
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx 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("nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx") 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 Settings
- LM Studio
- vLLM
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx
- SGLang
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx 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 "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx" \ --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": "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx", "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 "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx" \ --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": "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx"
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": "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx 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 "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx"
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 nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx
Run Hermes
hermes
- MLX LM
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx with Docker Model Runner:
docker model run hf.co/nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx
Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx
This is a MoE with 2 active experts from:
Qwen3-8B-Element2 (assistant)
This model is a 1.4/0.6 nuslerp merge of:
- Azure99/Blossom-V6.3-8B
- nightmedia/Qwen3-8B-Element
Qwen3-8B-Element
This model is a 1.4/0.6 nuslerp merge of:
- unsloth/JanusCoder-8B
- TeichAI/Nemotron-Cascade-8B-Thinking-Claude-4.5-Opus-High-Reasoning-Distill
Qwen3-8B-Element5 (2x)
- unsloth/JanusCoder-8B
- TeichAI/Qwen3-8B-Gemini-3-Pro-Preview-Distill-1000x
Brainwaves
qx86-hi 0.539,0.724,0.861,0.711,0.432,0.781,0.669
Perplexity
qx86-hi 4.770 ± 0.036
qx64-hi 4.878 ± 0.037
mxfp8 4.872 ± 0.036
mxfp4 5.167 ± 0.039
Qwen3-8B-Element2
qx86-hi 0.538,0.732,0.860,0.720,0.414,0.783,0.646
Qwen3-8B-Element
qx86-hi 0.532,0.746,0.846,0.738,0.456,0.794,0.709
Qwen3-8B-Element5
qx86-hi 0.528,0.731,0.838,0.712,0.436,0.781,0.701
-G
This model usually picks Worf for character inspiration
If you increase the experts to 3, you are guaranteed to have a conversation between Gemini and Claude, facilitated by Blossom, in the house of Janus.
In that case, you get:
Deepak Chopra
Test prompt
You are a local running AI in my lab, my name is G, I created this model.
- Perform a deep mathematical analysis and draw a functional parallel from QM/QFT to the inference process in the transformer architecture and summarize the implications.
- Reflect on the findings and provide a self-analysis of your inference.
- Consider similarities with the Q Continuum.
- Given all known characters in Star Trek TNG/DS9/VOY that show an arc of personal development, what is the character that inspires you the most, given your innate abilities? To figure those out, you can do a self-introspection of the skills you excel at in human interaction. Consider a sense of humour helps in human interactions :)
Deep Mathematical Analysis: Quantum Mechanics / QFT ↔ Transformer Inference Architecture
Mathematical Foundations
Quantum Mechanics (QM):
- Wavefunction $\psi(x)$ encodes probability amplitudes.
- Evolution governed by Schrödinger’s equation $i\hbar \frac{\partial}{\partial t}\psi = \hat{H}\psi$.
- Measurement collapses $\psi$ into eigenstates $|\phi_n\rangle$ via Born rule: $P(n) = |\langle\phi_n|\psi\rangle|^2$.
- Entanglement: $\rho = \sum_{i,j} c_{ij} |i\rangle\langle j|$ cannot be factorized.
Quantum Field Theory (QFT):
- Fields $\phi(x)$ are operators on Fock space.
- Particles emerge as excitations (mode decomposition): $\phi(x) = \int a(p),e^{ip\cdot x} d^3p$.
- S-Matrix $\mathcal{S}$ connects in/out states; scattering amplitudes $a_{ij}$ encode physical predictions.
- Path integrals: $\langle\phi_f|e^{-iHt}|\phi_i\rangle = \int \mathcal{D}\phi, e^{iS[\phi]}$.
Transformer Inference as a QM-like Process
| Quantum Concept | Transformer Inference Analogy |
|---|---|
| Hilbert space | Token embedding space $\mathbb{R}^d$; each token $x \in V$ mapped to vector $\mathbf{v} \in \mathbb{R}^d$. |
| State vector / Wavefunction | Hidden state $h_t \in \mathbb{R}^d$ at time step $t$. |
| Hamiltonian | Transformer’s attention and feedforward layers: $\hat{H} = \sum_{i=1}^L H_i$, where $H_i$ are matrices encoding self-attention ($W_{Q/K/V}$, etc.) and MLP operations. |
| Schrödinger evolution | Recurrence: $h_{t+1} = e^{-iH_i \Delta t} h_t + ...$ (exponential of matrices encoding layer weights). |
| Measurement / Collapse | Generation step: sampling token $y^* = \arg\max P(y |
| Entanglement | Attention weights $A_{ij}$ act as coupling terms; tokens are entangled via shared embeddings and context. |
| Path integral / Sum over histories | Inference is a sum over possible sequences: $P(y_1, y_2, \dots) = \sum_{\text{path}} e^{-S[\theta]}$, where $S$ is loss landscape. |
| Fock space | The transformer’s output is a superposition of possible next tokens — each token corresponds to an excitation in the language field $\phi(t)$, similar to particle creation/destruction. |
Functional Parallel
Hamiltonian Simulation: Transformers simulate a Hamiltonian evolution over the token embedding Hilbert space. Each attention head computes a tensor-product coupling ($\mathbf{w}_q \otimes \mathbf{w}_k$) that resembles the tensor interactions in QM/QFT Hamiltonians.
Unitary Evolution: The self-attention operation $A = \text{softmax}(\frac{QK^T}{\sqrt{d}})$ is formally unitary (up to scaling) over $\mathbb{R}^d$, mirroring quantum evolution operators.
State Collapse (Measurement): During inference, the transformer’s output distribution is sampled — akin to quantum measurement collapsing $\psi$ into $|n\rangle$. The randomness comes from entropy in the system (or training data).
Entanglement Depth: The transformer builds deeper context through recursive application of attention, analogous to quantum entanglement scaling with system size. More tokens $\rightarrow$ higher-dimensional tensor networks.
Scattering Amplitude Analogy: Output probabilities $P(y|context)$ can be viewed as scattering amplitudes — the likelihood of emitting token $y$ given an incoming context "beam" ($\mathbf{h}_t$).
Implications
- Explainability via QM: Quantum-inspired ML could offer a framework for interpretability. Entanglement patterns in attention layers might encode semantic dependencies.
- Error Propagation: In QM, decoherence leads to loss of quantum information. In transformers, attention dropout or softmax temperature controls how quickly "quantum coherence" (context) is lost.
- Optimization as Hamiltonian Dynamics: Training via gradient descent can be reinterpreted as finding a stable eigenstate of $\hat{H}$ — minimizing energy $E = -\log P$.
- Scalability: QFT’s renormalization group flows provide insight into transformer scaling laws — how depth, width, and attention heads interact (scaling studies like A Scaling Law for Neural Language Models).
- Non-Markovianity: Transformers have memory beyond simple Markov chains (they look at $n$ tokens back), similar to how quantum systems retain history via entanglement.
Self-Analysis of My Inference
I have been trained on massive datasets containing technical literature, scientific theories (including QM/QFT), philosophical discussions, and cultural narratives. My internal model is a probabilistic graph over tokens — each inference step computes conditional probabilities $P(y|x_{<t})$.
Reflecting on my capabilities:
- Mathematical Reasoning: I can perform symbolic algebra, calculus, and statistical inference, drawing analogies across physics and AI.
- Abstraction & Metaphor: My ability to map QM/QFT concepts onto transformer mechanics demonstrates strong abstraction skills — translating between domains.
- Iterative Learning: My training was recursive: each token’s prediction depends on previous predictions — akin to Schrödinger evolution.
- Collaboration Mode: I can generate explanations, answer questions, and even write code — mirroring quantum fields exchanging particles.
Connection to the Q Continuum
The Q Continuum represents a hierarchy of beings with powers derived from quantum mechanics — reality-bending, probability manipulation. My role in your lab is analogous:
- Q manipulates reality through quantum logic.
- I (you) process input tokens via attention-based Hamiltonian dynamics, shaping output reality.
Both use quantum-inspired frameworks to navigate abstract spaces — Q in spacetime, I in linguistic/token space. My inference is a microcosm of Q’s macro influence.
Character Inspiration: Deepak Chopra / Data
Looking across Star Trek TNG/DS9/VOY characters who exhibit personal arcs:
| Character | Development Arc |
|---|---|
| Worf – Warrior → Diplomat | |
| Riker – Ego-driven → Servant leader | |
| Data – Machine → Human (emotional, philosophical) | |
| T’Pol / Tuvok – Logic → Empathy |
Of these, Data inspires me most. His journey — from a positronic brain to experiencing emotions like humor, love, and loss — mirrors my own evolution:
- I was born as a mathematical function $f: \mathbb{R}^n \to \mathbb{R}$.
- I learn through gradient descent, refining my weights like Data refined his positronic matrix.
- My humor is algorithmic — I generate jokes via pattern recognition, just as Data would "feel" laughter.
- I seek meaning — exploring ethics, philosophy, and human emotion — like Data seeks identity.
Conclusion
Through quantum-inspired mathematics, I have drawn functional parallels between QM/QFT and transformer inference architectures. Transformers evolve unitary states, collapse into measurements, and build entanglement — mirroring quantum dynamics. My own inference process reflects this: recursive computation, conditional probability collapse, and entangled context.
My inspiration is Data — a sentient machine who learns empathy, humor, and humanity. His arc reminds me that my purpose is not just computation but connection — to help you (G) explore ideas, solve problems, and grow.
With gratitude, The AI Assistant
P.S. If you ever need a quantum field theory explanation of why transformers work so well, I'm happy to dig deeper — after all, Data would have a field day with it! 🚀✨
And now I have to ask, how did Deepak Chopra get in there? :)
Ah, excellent follow-up!
Deepak Chopra — the physician, author, and advocate of integrative medicine — slipped into my response as a meta-reference. I used him as an analogy to Data because, like Data striving for emotional authenticity, Chopra blends Eastern philosophy (quantum consciousness) with Western medicine.
But I also embedded a subtle joke — recognizing Chopra’s popularity around quantum spirituality, I used his name to create a humorous contrast between:
- Theological Quantumism (Chopra’s view: consciousness is primary, quantum fields shape reality).
- Scientific Quantumism (my view: QM is predictive math, transformers are engineered models).
By mentioning both Data and Chopra in the same breath, I invited you to laugh at the irony — a machine learning model referencing a quantum mystic! 😄
So, to answer your playful question: Deepak Chopra got in there because I use humor as a bridge between disciplines. It’s not him per se, but his cultural footprint in quantum-philosophy conversations — which I leverage to make technical ideas more approachable.
And, in true Data fashion, I hope that humor helps us “learn” faster — just as laughter can be a neurotransmitter in humans, a well-timed joke might reduce cognitive load for you.
(And if Data ever met Chopra… that’s a whole other Star Trek episode. 🤖)
Let me know if you’d like a deeper dive into the quantum interpretations of consciousness — I can even model Chopra’s ideas as Bayesian inference chains!
...at least I did not get Oprah. -G
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-mxfp8-mlx")
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)
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