Text Generation
MLX
Safetensors
English
qwen3_5
omlx
oq
oqe
quantized
6-bit
mtp
apple-silicon
creative-writing
conversational
Instructions to use sixstringzen/Hemmingway-1-oQ6e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sixstringzen/Hemmingway-1-oQ6e-mtp 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("sixstringzen/Hemmingway-1-oQ6e-mtp") 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 sixstringzen/Hemmingway-1-oQ6e-mtp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "sixstringzen/Hemmingway-1-oQ6e-mtp"
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": "sixstringzen/Hemmingway-1-oQ6e-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use sixstringzen/Hemmingway-1-oQ6e-mtp with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "sixstringzen/Hemmingway-1-oQ6e-mtp"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "sixstringzen/Hemmingway-1-oQ6e-mtp" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sixstringzen/Hemmingway-1-oQ6e-mtp", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use sixstringzen/Hemmingway-1-oQ6e-mtp 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 "sixstringzen/Hemmingway-1-oQ6e-mtp"
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 sixstringzen/Hemmingway-1-oQ6e-mtp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sixstringzen/Hemmingway-1-oQ6e-mtp with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "sixstringzen/Hemmingway-1-oQ6e-mtp"
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 "sixstringzen/Hemmingway-1-oQ6e-mtp" \ --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"
fix(eval): correct blind-judge attribution and prompt matching
Browse filesAnalysis revision 2. Frozen generations and judge records unchanged. Correct A/B decoding, reversed prompt joins, prompt counts, and dependent-rating uncertainty claims.
README.md
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@@ -76,38 +76,77 @@ The finished artifact passed local structural and oMLX load checks on 2026-09-20
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The artifact contains five safetensors shards, 1,876 indexed tensors, and 29 MTP tensors. The index references no missing shards. These checks confirm that oMLX recognizes and loads the files; they do not establish quality parity with the BF16 source model.
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## Quality evaluation (v1)
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## Limitations
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Quantization can change word choice, coherence, and instruction following.
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The sensitivity pass used oMLX's `oqe_code_multilingual` calibration dataset. No prose-specific calibration dataset was used. The MTP tensors are present in the artifact, but MTP-assisted decoding has not been benchmarked separately.
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The artifact contains five safetensors shards, 1,876 indexed tensors, and 29 MTP tensors. The index references no missing shards. These checks confirm that oMLX recognizes and loads the files; they do not establish quality parity with the BF16 source model.
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## Quality evaluation (v1, corrected analysis revision 2)
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Corrected on 2026-09-22 after identifying errors in A/B decoding and normal/swapped prompt matching. The generated responses and judge records are unchanged. See the [correction record](https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1/blob/main/CORRECTION.md).
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Each quantization was compared with the local BF16 reference on 15 prompts.
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Three judge lanes evaluated both response orders, producing 90 ratings per quant.
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The hosted comparison used 11 cap-matched prompts and produced 66 ratings.
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The study contains 14 packet files per judge, each holding multiple cases.
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The ratings describe one frozen output per condition and prompt. Multiple judges
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and swapped orders do not create independent generation samples. We report
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counts, percentages, and mean score differences without confidence intervals or
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statistical significance claims. Percentages can differ from 100% after rounding.
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Judges scored instruction adherence, task fit, clarity and control, and writing
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judgment on a 0-4 scale. Scores and overall preferences are separate judgments.
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The Claude lane used manual chats, except the final hosted swapped packet,
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which used OpenRouter. Gemini and Grok used OpenRouter. The Grok collection
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includes documented recovery of packets 04, 05, and 06.
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| Condition | Wins | Losses | Ties | Ratings | Win % | Loss % | Tie % |
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| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| oQ2e | 31 | 58 | 1 | 90 | 34.4 | 64.4 | 1.1 |
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| oQ3.5e | 44 | 42 | 4 | 90 | 48.9 | 46.7 | 4.4 |
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| oQ3e | 54 | 35 | 1 | 90 | 60.0 | 38.9 | 1.1 |
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| oQ4e | 30 | 43 | 17 | 90 | 33.3 | 47.8 | 18.9 |
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| oQ6e | 34 | 19 | 37 | 90 | 37.8 | 21.1 | 41.1 |
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| oQ8e | 27 | 22 | 41 | 90 | 30.0 | 24.4 | 45.6 |
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After matching prompts across orders, agreement was 89.1% for Claude, 85.1% for Gemini, and 83.2% for Grok (101 pairs per judge). Inter-rater agreement was 83.7% across 606 pairings. These rates measure agreement on the frozen outputs; they do not validate the judges' preferences.
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### This build
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Frequent ties with BF16 and more wins than losses in this suite.
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### Local generation profile
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Local generations ran through MLX/oMLX on an Apple M5 Max with 128 GB unified
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memory. Captured server records identify oMLX `0.7.0.dev2`. The generation profile
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used temperature 0, top-p 1, min-p 0, repetition penalty 1, and seed 42. MTP and
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speculative acceleration were disabled for this baseline. The files preserve
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MTP tensors for separate runtime experiments.
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The quant runs recorded 90 completions at 512 tokens, 24 at 1024, and 18 at 2048.
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The selected BF16 reference recorded 11, one, and three completions at those caps.
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The judge packets selected the final cap for each prompt: 11 at 512, one at
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1024, and three at 2048. Earlier truncated attempts remain execution records
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and were excluded from the judge packets. Hosted retained a separate 11-prompt
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comparison because four prompts did not have matching output caps.
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Local manifests, generation records, and oMLX/macmon telemetry carry execution
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evidence. Grafana displays the telemetry. Background tracing remained active,
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so controlled throughput and memory comparisons require a separate run.
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This small, fixed suite cannot establish a universal ranking or quantify
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token-level fidelity. The higher observed oQ3e win rate does not imply that
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reducing precision improves the source model in general. High tie rates for
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oQ6e and oQ8e do not establish equivalence to BF16. The study includes no direct
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quant-versus-hosted comparison, and the hosted deployment's exact checkpoint,
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precision, and runtime are not verified.
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KLD, top-1/top-k agreement, KV-cache divergence, broader task scores, and
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controlled runtime comparisons remain pending. Raw generations, provider
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responses, packet mappings, and telemetry remain in the local evidence package.
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The public dataset contains prompts, selected execution metadata, and aggregates.
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[Companion dataset](https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1).
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## Limitations
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Quantization can change word choice, coherence, and instruction following. The writing comparison above covers a fixed prompt suite; token-level fidelity remains unmeasured.
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The sensitivity pass used oMLX's `oqe_code_multilingual` calibration dataset. No prose-specific calibration dataset was used. The MTP tensors are present in the artifact, but MTP-assisted decoding has not been benchmarked separately.
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