Text Generation
MLX
English
lora
fine-tuned
multichain
web3
cross-chain
defi
wrapped-events
purple-squirrel
adapter
deepseek-r1
deepseek
reasoning
8b
apple-silicon
local-inference
blockchain
Instructions to use purplesquirrelnetworks/purple-squirrel-r1-multichain-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use purplesquirrelnetworks/purple-squirrel-r1-multichain-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("purplesquirrelnetworks/purple-squirrel-r1-multichain-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 purplesquirrelnetworks/purple-squirrel-r1-multichain-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 "purplesquirrelnetworks/purple-squirrel-r1-multichain-lora" --prompt "Once upon a time"
Upload adapter_config.json with huggingface_hub
Browse files- adapter_config.json +40 -0
adapter_config.json
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{
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"adapter_path": "/Volumes/Virtual Server/projects/psm-ops/multichain-day-model/model-output/mlx-adapters",
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"batch_size": 1,
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"config": null,
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"data": "/Volumes/Virtual Server/projects/psm-ops/multichain-day-model/model-output/mlx-training-data",
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"fine_tune_type": "lora",
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"grad_accumulation_steps": 1,
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"grad_checkpoint": true,
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"iters": 200,
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"learning_rate": 1e-05,
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"lora_parameters": {
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"rank": 8,
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"dropout": 0.0,
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"scale": 20.0
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},
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"lr_schedule": null,
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"mask_prompt": false,
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"max_seq_length": 1024,
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"model": "mlx-community/DeepSeek-R1-Distill-Llama-8B-4bit",
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"num_layers": 4,
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"optimizer": "adam",
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"optimizer_config": {
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"adam": {},
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"adamw": {},
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"muon": {},
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"sgd": {},
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"adafactor": {}
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},
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"project_name": null,
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"report_to": null,
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"resume_adapter_file": null,
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"save_every": 100,
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"seed": 42,
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"steps_per_eval": 50,
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"steps_per_report": 10,
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"test": false,
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"test_batches": 500,
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"train": true,
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"val_batches": 25
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}
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