Text Generation
MLX
English
structured-generation
parallel-decoding
constrained-decoding
apple-silicon
classification
json
Instructions to use botp/Qwen-2.5-1B-RLCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use botp/Qwen-2.5-1B-RLCD 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("botp/Qwen-2.5-1B-RLCD") 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 botp/Qwen-2.5-1B-RLCD with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "botp/Qwen-2.5-1B-RLCD" --prompt "Once upon a time"
- Atomic Chat
File size: 663 Bytes
84f0c1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | """
Parallel Constrained Structured Generation Engine.
"""
from core.schema import StructuredSchema, FieldDefinition
from core.engine import (
get_engine,
run_parallel_generation,
run_naive_generation,
stream_naive_generation,
# Backward compatibility
run_rlcd_generation,
)
from core.prompt_builder import (
build_naive_json_prompt,
build_parallel_field_prompts,
)
__all__ = [
"StructuredSchema",
"FieldDefinition",
"get_engine",
"run_parallel_generation",
"run_naive_generation",
"stream_naive_generation",
"build_naive_json_prompt",
"build_parallel_field_prompts",
"run_rlcd_generation",
]
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