Instructions to use jrad123777/effect-qwen36-35b-write-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jrad123777/effect-qwen36-35b-write-lora with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("jrad123777/effect-qwen36-35b-write-lora") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use jrad123777/effect-qwen36-35b-write-lora with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "jrad123777/effect-qwen36-35b-write-lora" --prompt "Once upon a time"
| base_model: mlx-community/Qwen3.6-35B-A3B-4bit | |
| library_name: mlx | |
| pipeline_tag: text-generation | |
| tags: | |
| - lora | |
| - mlx | |
| - effect-ts | |
| - typescript | |
| - code-generation | |
| license: apache-2.0 | |
| # effect-qwen36-35b-write-lora β Effect v4 TypeScript champion LoRA (`v7s43_i200`) | |
| Rank-8 LoRA adapter on **`mlx-community/Qwen3.6-35B-A3B-4bit`** that writes idiomatic | |
| **Effect v4** (`effect@4.0.0-beta.8x`) TypeScript: `Context.Service`/`Layer` services, | |
| `Effect.gen` pipelines, `Schema`, `Data.TaggedError`, and typed-error seams β verified by a | |
| best-of-N + TypeScript-compiler product harness, not by eyeballing. | |
| ## Result β the served PRODUCT (not raw greedy) | |
| - **best-of-16 + tsc gate: 23/24** on a frozen held-out set. This is the deliverable metric. | |
| - Judged on the served product pipeline (sample N β import-resolver β `tsc --strict`), **never | |
| raw single-greedy**: raw greedy is ceiling-blocked at ~9.67/24; the compiler verifier is what | |
| lifts it to 23/24. (A perfect 0/1 verifier means a returned "compiles" is genuinely verified.) | |
| ## Adapter | |
| - LoRA: rank 8, scale 20, dropout 0, 16 layers; lr 1e-5; warm-started from a continued-pretrain | |
| adapter. Apply **unfused** on top of the 4-bit base. | |
| - `adapters.safetensors` = 1,025,848,752 bytes (the byte-intact champion). | |
| ## Use (MLX) | |
| ```bash | |
| python -m mlx_lm generate \ | |
| --model mlx-community/Qwen3.6-35B-A3B-4bit \ | |
| --adapter-path . \ | |
| --prompt "Write an Effect service that fetches a user by id over HTTP, with a typed NotFound error." | |
| ``` | |
| For the actual deliverable, serve it behind a best-of-N + `tsc` gate (the raw adapter alone is not | |
| the product). The same champion also serves a surgical **EDIT** lane (best-of-N SEARCH/REPLACE β | |
| apply β `tsc` gate), tsc-verified. | |
| ## Related | |
| - Fused full model: `jrad123777/effect-qwen36-35b-mlx` Β· GGUF: `jrad123777/effect-qwen36-35b-gguf` | |
| - Companion React+seam LoRA: `jrad123777/effect-qwen36-35b-react-lora` | |