Instructions to use redhillsmediafl/Longleaf-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redhillsmediafl/Longleaf-35B-A3B 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("redhillsmediafl/Longleaf-35B-A3B")

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 redhillsmediafl/Longleaf-35B-A3B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "redhillsmediafl/Longleaf-35B-A3B"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "redhillsmediafl/Longleaf-35B-A3B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use redhillsmediafl/Longleaf-35B-A3B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "redhillsmediafl/Longleaf-35B-A3B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "redhillsmediafl/Longleaf-35B-A3B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "redhillsmediafl/Longleaf-35B-A3B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use redhillsmediafl/Longleaf-35B-A3B 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 "redhillsmediafl/Longleaf-35B-A3B"

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 redhillsmediafl/Longleaf-35B-A3B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use redhillsmediafl/Longleaf-35B-A3B with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "redhillsmediafl/Longleaf-35B-A3B"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "redhillsmediafl/Longleaf-35B-A3B" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Longleaf-35B-A3B

In development. Weights may be updated at any time.

Longleaf is our local model for coding and agent work: repository edits, debugging, structured tools, and clear answers.

Built on Qwen3.6-35B-A3B.

35B total / 3B active parameters.
MLX 4-bit weights.
262,144-token configured context.
Development hardware: M1 Ultra, 64 GB unified memory, 48-core GPU.
Splash thinking levels: none, low, medium, high, xhigh, max.

Coding Benchmarks

EvalPlus 0.3.1, 164 tasks, pass@1. Thinking off, greedy sampling, 2,048-token output cap, 262,144-token configured context. Generation used our M1 Ultra Splash runtime.

Model	HumanEval	HumanEval+	Median generation time	CLI sample (5 tasks)
Qwen3.6 base	95.1%	90.9%	1.19 s	1/5
Longleaf	96.3%	91.5%	0.97 s	1/5
Longleaf Abliterated	95.7%	90.9%	0.94 s	1/5

Prompts: 159 tokens median, 77–441 range. Median output lengths: 231 / 198 / 195 tokens, respectively.

CLI sample: five Terminal-Bench 2 tasks on ARM64, thinking off. Per task: 300 seconds, 24 model calls, 32 tool calls, 2,048 output tokens per call.

Pi Coding Run

Our M1 Ultra Splash runtime, medium thinking, model already loaded:

Model calls	Prompt tokens per call	Output tokens	Decode	First token
4	1,731–2,236	439 total	104–130 tokens/s	1.3 s first; 0.2–0.3 s cached

Read, edit, and test completed in about 6 seconds, with 4/4 tests passing.

Measured on a cached coding task.

Run

pip install -U mlx-lm
mlx_lm.generate --model redhillsmediafl/Longleaf-35B-A3B --prompt "Help me debug this code."

License

Apache 2.0. This release contains modified model weights, based on Qwen3.6-35B-A3B and the MLX conversion. Review generated code before running it and restrict agent tool permissions.

Abliterated version · Red Hills Media · GitHub

Downloads last month: 68

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for redhillsmediafl/Longleaf-35B-A3B

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(409)

this model

Finetunes

1 model