Instructions to use juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit 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("juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit")

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 juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit"

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": "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit"

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 "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit" \
  --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"

MLX LM

How to use juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit 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 "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit"

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 juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit

Run Hermes

hermes

juanquivilla commited on May 7

Commit

1b04172

verified ·

1 Parent(s): 8152256

soup_30: composite=89.45 — see model card for benchmark deltas vs v45

Browse files

Files changed (2) hide show

README.md +4 -4
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ tags:
 pipeline_tag: text-generation
 ---
-# SottoASR Transcript Cleanup — LFM2.5-350M MLX 5-bit (v55)
 [sottoasr.app](https://sottoasr.app) · [Full precision (bf16)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m) · [MLX 4-bit (smaller)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-4bit)
@@ -23,13 +23,13 @@ pipeline_tag: text-generation
 MLX 5-bit affine quantization of [juanquivilla/sotto-cleanup-lfm25-350m](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m). Recommended for Apple Silicon — best size/quality trade-off.
-## What's new in v55
-v55 extends v45 with targeted training data for five failure modes (multi-number sentences,
 year-context drift, disconnected number lists, within-input duplicates, long-form preservation),
 each generated programmatically and audited with a Qwen3.6-27B judge.
-| Metric | v45 | **v55** |
 |---|---:|---:|
 | Number accuracy | 95.9% | **96.5%** |
 | Adversarial benchmark (greedy) | 76% | **86%** |

 pipeline_tag: text-generation
 ---
+# SottoASR Transcript Cleanup — LFM2.5-350M MLX 5-bit (soup_30)
 [sottoasr.app](https://sottoasr.app) · [Full precision (bf16)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m) · [MLX 4-bit (smaller)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-4bit)
 MLX 5-bit affine quantization of [juanquivilla/sotto-cleanup-lfm25-350m](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m). Recommended for Apple Silicon — best size/quality trade-off.
+## What's new in soup_30
+soup_30 extends v45 with targeted training data for five failure modes (multi-number sentences,
 year-context drift, disconnected number lists, within-input duplicates, long-form preservation),
 each generated programmatically and audited with a Qwen3.6-27B judge.
+| Metric | v45 | **soup_30** |
 |---|---:|---:|
 | Number accuracy | 95.9% | **96.5%** |
 | Adversarial benchmark (greedy) | 76% | **86%** |

model.safetensors CHANGED Viewed

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