MagiSeek-Pro-V1

MagiSeek-Pro-V1

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Overview

MagiSeek-Pro-V1 is a model checkpoint packaged for compatible Hugging Face runtimes, published by groxaxo. It is intended for open-source evaluation, reproducible experimentation, and compatible local or hosted inference workflows. The wording below is deliberately limited to what can be verified from this repository's metadata and artifacts.

At a glance

What is included

• *.safetensors (10 files)
• config.json
• generation_config.json
• tokenizer.json
• tokenizer_config.json
• chat_template.jinja
• Additional configuration, tokenizer, processor, or shard files (17 visible artifacts total)

Quick start

Getting started

Start with the upstream library named in the repository metadata and keep all configuration, tokenizer, processor, and weight files together. This repository is an artifact release, so the source project remains the authoritative reference for task-specific loading code.

Compatibility and responsible use

• Use a runtime that explicitly supports this format, architecture, and modality.
• Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
• Review the source model card and license before redistribution or deployment.
• Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
• Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.

Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.

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24B params · Mistral architecture · bf16 merged weights · 131k native context

How he was born

Deep in a rack of three RTX 3090s, WarlordHermes/Magidonia-24B-v4.3-creative-ORPO sat quietly — a solid creative writer, but a little too polite to touch a terminal. Over five curriculum phases of QLoRA (rank 64, rslora, target-all-linear), he was put through school one subject at a time:

• Phase 1–2: foundations, reformatted instruction-following at 2k/4k/8k context, reviewed and re-run in _v2 passes when the mask logic didn't add up.
• Phase 3: a second identity pass, frozen and checked before moving on.
• Phase 4 (agentic): real tool-execution transcripts (hermes-agent-reasoning-traces) plus a small hand-reviewed architecture-QA supplement distilled from Claude Opus 4.6 reasoning traces — this is where he learned to actually call things instead of just describing them.
• Phase 5 (deepseek continuation): a narrow, conservative continuation (979 curated examples, 1 epoch, lower LR, packing off until the mask was hand-verified) chaining off the phase-4 agentic adapter, sharpening DeepSeek-style step-by-step reasoning without overwriting the personality underneath.

Each phase's LoRA adapter was chained onto the previous one — never onto the raw base — so nothing before it got forgotten. The final p5-deepseek adapter was then merged straight into the base weights in full bf16 (no quantization, no rounding shortcuts) to produce this checkpoint: a clean, full-precision snapshot of everything he learned, suitable as the master copy for every downstream quantization (GGUF, GPTQ, AWQ, ...) that follows.

He answers to MagiSeek now — half Magidonia's creative instincts, half a DeepSeek-flavored reasoner who seeks the tool call before the excuse.

Model details

• Architecture: Mistral (MistralForCausalLM), 40 layers, hidden 5120, 32 heads / 8 KV heads (GQA), rope_theta 1e9
• Context: up to 131,072 tokens native
• Precision: bf16 (merged, full precision — no PEFT adapter required at inference time)
• Base model: WarlordHermes/Magidonia-24B-v4.3-creative-ORPO
• Training: 5-phase chained QLoRA curriculum (axolotl), final phase = deepseek-style agentic reasoning continuation
• License: Apache 2.0

Benchmarks (this checkpoint, merged bf16)

Intended use

General instruction following, creative writing, and agentic/tool-use tasks requiring long-context reasoning. This is the bf16 merged base — recommended as the source for further quantization (GGUF/GPTQ/AWQ) rather than for direct low-VRAM deployment.

Lineage

WarlordHermes/Magidonia-24B-v4.3-creative-ORPO
  └─ magidonia_curric_p1 / p1_v2
      └─ magidonia_curric_p2 / p2_v2
          └─ magidonia_curric_p3 / p3_v2 (frozen)
              └─ magidonia_curric_p4_agentic_v1 / v2 (frozen)
              └─ magidonia_curric_p5_deepseek_v1   <-- merged here, bf16

Using the full-precision checkpoint

This repository contains the bf16 merged weights as the canonical full-precision release. The model is approximately 48 GB in bf16, so direct serving normally needs multiple GPUs or CPU offload.

Recommended: vLLM OpenAI-compatible server

The included chat_template.jinja matches the model's Hermes-style <tool_call> JSON. Use the Hermes parser so OpenAI clients receive structured tool_calls rather than raw tool-call text:

vllm serve groxaxo/MagiSeek-Pro-V1 \
  --dtype bfloat16 \
  --tensor-parallel-size 3 \
  --max-model-len 131072 \
  --chat-template chat_template.jinja \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --host 0.0.0.0 --port 8000

For OpenCode, configure an OpenAI-compatible provider:

Base URL: http://HOST:8000/v1
Model:    groxaxo/MagiSeek-Pro-V1

For agentic use, prefer a temperature around 0.6–0.7 rather than 0.0.

Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "groxaxo/MagiSeek-Pro-V1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Explain what you can do."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)
output = model.generate(inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))

When using Transformers directly, parse the model's <tool_call>{...}</tool_call> output or use an OpenAI-compatible serving layer such as vLLM for automatic structured tool-call responses. Reasoning and tool calls should not be displayed as ordinary user-facing text.

Template

• chat_template.jinja — the model-native Hermes tool-use template for this full-weight checkpoint. It is intended for Transformers-compatible serving and vLLM; use the vLLM hermes tool parser for OpenCode.