Instructions to use halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32 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("halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32")

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

How to use halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32"

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": "halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32 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 "halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32"

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 halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32

Run Hermes

hermes

MLX LM

How to use halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3-Next-80B-A3B-Instruct — MLX 5-bit (group size 32)

Summary. This is a 5-bit (Q5) MLX quantization of Qwen3-Next-80B-A3B-Instruct with group size 32. Built for Apple Silicon with Metal acceleration.

Base model: Qwen/Qwen3-Next-80B-A3B-Instruct (apache-2.0)
Quantization: MLX Q5, q_group_size=32 (some tensors may remain 16-bit for stability)
Files: MLX weight shards + config.json; tokenizer files included for drop-in use
Intended use: local inference / research on M-series Macs
Not intended for: safety-critical decisions; outputs may be inaccurate or biased

Requirements

Built for Apple Silicon with Metal acceleration.

Memory: ≥96 GB recommended for comfortable headroom at large context lengths.

How to use (MLX)

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32")
print(generate(
    model, tokenizer,
    prompt="Explain the Chudnovsky algorithm to compute π.",
    max_tokens=256, max_kv_size=512
))

python -m mlx_lm generate --model halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32 \
  --prompt "Explain the Chudnovsky algorithm to compute pi." \
  --max-kv-size 512 --max-tokens 256

Evaluation

Perplexity (PPL) streaming evaluation on WikiText-2 (raw, test); fast preset with window=stride=4096, ~100k tokens, EOS inserted between docs.

Variant	PPL (ctx=4096, fast)
MLX bf16 (reference)	5.14
MLX 6-bit (gs=64)	5.14 (≈0.0% vs bf16)
MLX 5-bit (gs=32)	5.20 (+1.2% vs bf16, +1.2% vs 6b/gs64)
MLX 4-bit (gs=64)	5.43 (+5.6% vs bf16, +5.6% vs 6b/gs64)

Notes:

Numbers from local MLX runs on Apple Silicon; small variations are expected with tokenizer details, logits dtype, and token subset.

Interpretation

6-bit gs64 matches bf16 on this corpus; use it when maximum quality is the goal.
5-bit gs32 is a balanced pick: near-par PPL with a smaller footprint and strong deterministic math behavior.
4-bit gs64 trades a modest quality drop for the smallest size; good for constrained machines.

Reproduce locally:

python python/scripts/test_perplexity-mlx.py \
  --model_path "/path/to/Qwen3-Next-80B-A3B-Instruct-5bit-gs32" \
  --fast --progress

Conversion details (provenance)

python -m mlx_lm convert \
  --hf-path Qwen3-Next-80B-A3B-Instruct \
  --mlx-path /path/to/Qwen3-Next-80B-A3B-Instruct-5bit-gs32 \
  -q --q-bits 5 --q-group-size 32

Some tensors (for example, embeddings/norms/router) may remain 16-bit for numerical stability.

Sibling & reference models

halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64
halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64

Limitations and biases

Outputs may be factually wrong or unsafe. Do not use for medical, legal, or financial decisions without human review.

License and credits

License: apache-2.0 (inherits from the base model)
Base model: Qwen/Qwen3-Next-80B-A3B-Instruct
Quantization: Halley AI Lab (MLX Q5, gs=32)
Please cite both the base model and this repository when you use the weights.

Downloads last month: 13

Safetensors

Model size

80B params

Tensor type

BF16

U32

MLX

Hardware compatibility

5-bit

Model tree for halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32

Base model

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