Instructions to use hectoreccles/h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hectoreccles/h 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("hectoreccles/h") 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
- MLX LM
How to use hectoreccles/h with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "hectoreccles/h"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "hectoreccles/h" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hectoreccles/h", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
h
Same weights as hermitdave/Hemmingway-1-MLX-4bit. The tokenizer strings for the chat and thinking markers are one character, and they still encode to the original token IDs.
Uniform 4-bit quantization of Altworld/Hemmingway-1, a 27B parameter hybrid-attention language model built on Qwen3.8-27B.
Upstream model: Altworld/Hemmingway-1 by Altworld, released under Apache-2.0.
Conversion: Quantized to MLX format using Hermes Agent with mlx-lm (uniform 4-bit, group size 64).
Architecture
| Component | Value |
|---|---|
| Parameters | 27B |
| Hidden size | 5120 |
| Layers | 64 |
| Attention heads | 24 (full) / 16 (linear) |
| KV heads | 4 (full) / 48 (linear) |
| Intermediate size | 17408 |
| Vocab size | 248,320 |
| Context length | 262,144 |
| Attention type | Hybrid (linear + full every 4th layer) |
| RoPE theta | 10,000,000 |
Quantization Details
| Property | Value |
|---|---|
| Method | Uniform 4-bit |
| Bits per weight | 4.501 |
| Group size | 64 |
| Dtype | bfloat16 |
Tokenizer markers
These replace the long control strings. Token IDs are unchanged, so the weights are not retrained.
| Token ID | Was | Now |
|---|---|---|
| 248045 | <|im_start|> |
≺ |
| 248046 | <|im_end|> |
≻ |
| 248068 | <think> |
⊏ |
| 248069 | </think> |
⊐ |
A prompt that used to start <|im_start|>system is now ≺system. The old long strings are ordinary text and do not map to those IDs anymore.
Quickstart
pip install -U mlx-lm
python3 -m mlx_lm.generate \
--model hectoreccles/h \
--prompt "Write a text to your landlord about the broken boiler." \
--max-tokens 512 --temp 1.0 --top-p 0.95
Benchmark Results (DeepEval, local run)
Benchmarked on the oQ4e quant of this model (not this 4-bit quant) in a head-to-head vs Ornith-1.5-35B oQ4e, DeepEval GEval (LLM judge), run 2026-09-23, oMLX on M3 Max. 50 cases per model; message-not-memo sampled 3 generations per prompt.
| Category | Hemmingway-1 oQ4e | Ornith-1.5 oQ4e |
|---|---|---|
| Directness | 1.00 | 0.78 |
| Human-Likeness | 1.00 | 0.72 |
| Empathy (EQ) | 0.72 | 0.85 |
| Hard asks — Actionability | 0.64 | 0.53 |
| Hard asks — Confidence | 0.62 | 0.75 |
| Message Not Memo | 0.71 | 0.82 |
| Story Quality | 0.94 | 0.96 |
~5% of generations hit transient server errors and were excluded.
Citation
@misc{hemmingway1,
title = {Hemmingway-1},
author = {Altworld},
year = {2026},
url = {https://huggingface.co/Altworld/Hemmingway-1},
}
License
Apache-2.0 (same as upstream).
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4-bit
Model tree for hectoreccles/h
Base model
Qwen/Qwen3.8-27B