Instructions to use TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-MLX-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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("TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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
- Pi
How to use TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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": "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-MLX-5bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-MLX-5bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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": "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-MLX-5bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 "TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-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 TalkMate/Qwen3.5-0.8B-parakeet-52k-FullFT-MLX-5bit
Run Hermes
hermes
Qwen3.5-0.8B Parakeet FullFT — MLX 5-bit
This is a 5-bit MLX conversion of
rdsm/Qwen3.5-0.8B-parakeet-52k-FullFT,
published by TalkMate for local
English transcript cleanup on Apple silicon.
The source model is a full fine-tune of Qwen/Qwen3.5-0.8B trained on
rdsm/parakeet-stt-redone.
It cleans already-transcribed text; it is not a speech-recognition model.
Provenance
| Source model | rdsm/Qwen3.5-0.8B-parakeet-52k-FullFT |
| Source revision | d1b8e0c7371779e7da322332ccea4a019b4c3910 |
| Source format | BF16 safetensors |
| Conversion tools | mlx-lm 0.31.3, MLX 0.32.0 |
| Quantization | Uniform affine, 5 bits, group size 64 |
| Effective quantization | 5.507 bits per weight |
| Weight size | 518,013,158 bytes |
| Weight SHA-256 | ce91877f73def5cd98d1a4a91030607db88849c1adb60a577c14948138a642ea |
The conversion was produced with:
hf download rdsm/Qwen3.5-0.8B-parakeet-52k-FullFT \
--revision d1b8e0c7371779e7da322332ccea4a019b4c3910 \
--local-dir source
mlx_lm.convert \
--hf-path source \
--mlx-path mlx-5bit \
-q \
--q-bits 5
TalkMate evaluation
TalkMate selected 5-bit after comparing the pinned BF16 source with local 8-bit, 5-bit, and 4-bit MLX conversions on a deliberately authored 21-case transcript-cleanup corpus.
The 5-bit artifact completed three runs through TalkMate's production path:
- 21 / 21 important requirements passed in every run;
- 6 / 8 nice-to-have requirements passed in every run;
- all 21 final outputs were byte-for-byte identical across the three runs;
- mean cleanup latency was 0.678 seconds and aggregate p95 was 0.936 seconds on a 16 GB M4 Mac with the transcription model unloaded.
Important requirements covered short fragments, punctuation, disfluency removal, Personal Vocabulary casing, numbers, negation, long-form meaning, instruction-like dictation, and preservation of both spoken course-correction clauses.
These are TalkMate application-path results, not an independent raw-model
benchmark. The path parses the model's cleaned_text JSON field and applies
TalkMate's vocabulary guard, faithfulness guard, and conservative
deterministic list formatter. The target 8 GB M1 memory test with Parakeet
Accurate resident remains outstanding.
Evaluation configuration
TalkMate used the source model's transcript-cleanup prompt contract with:
- thinking disabled;
- temperature
0.2; - a 160-token benchmark limit;
- JSON output read from the
cleaned_textfield.
Applications integrating this model should independently validate output faithfulness and fall back to the original transcript if meaningful content is added, removed, or changed.
Limitations
- The TalkMate corpus is intentionally small and product-specific.
- Results do not establish quality for every accent, ASR engine, language, or long-form transcript.
- This quantization is published by TalkMate and is not the author's separately referenced 8-bit conversion.
- Quantized models can behave differently from their source checkpoint.
- The model should not be used without application-level checks when exact transcript fidelity matters.
License and attribution
The source checkpoint and this conversion are distributed under the
Apache License 2.0. Please retain attribution to the source model author,
the Qwen/Qwen3.5-0.8B base model, and the original training dataset.
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