MagiSeek V1.1
Overview
MagiSeek-V1.1 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
| Field | Details |
|---|---|
| Format | Transformers |
| Source / base | groxaxo/MagiSeek-Pro-V1 |
| Intended task | text-generation |
| License | apache-2.0 |
What is included
*.safetensors(10 files)config.jsongeneration_config.jsontokenizer.jsontokenizer_config.jsonchat_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.
A field-tested continuation of MagiSeek-Pro-V1
MagiSeek V1.1 is a carefully merged continuation of MagiSeek-Pro-V1, shaped around the kind of work that happens after the demo: reading a messy request, keeping the thread of intent, reasoning through a change, and producing a useful next action.
The story behind this release is deliberately simple. MagiSeek-Pro-V1 was the strong starting voice. We then placed it in a long, demanding practice loop of cleaned coding-agent and tool-aware conversations. The model learned through a rank-64 rsLoRA continuation, and this release captures the first verified checkpoint of that journey: step 800, before the run was intentionally stopped.
That makes V1.1 a snapshot with character, not a claim of finality. It is the moment where the new habits are visible while the original MagiSeek style is still close to the surface. Use it as an experimental open model, a base for evaluation, or a reference point for future training.
What is included
- Full merged model weights; no adapter is required.
- bfloat16 safetensors, split into 10 shards.
- Native MagiSeek tokenizer and chat template.
- Merge source manifest in
MERGE_SOURCE.json.
Training snapshot
- Base:
groxaxo/MagiSeek-Pro-V1 - Base revision:
c7a0a437ca849bf4e3286fe5aae7951185974651 - Training method: QLoRA / rsLoRA, rank 64, alpha 64
- Target modules: attention and MLP projections
- Context length: 8,192 tokens
- Objective: assistant-only supervised fine-tuning
- Checkpoint: global step 800, approximately 0.187 epoch of the planned run
- Hardware: three NVIDIA RTX 3090 GPUs
The run was stopped before its planned 10 epochs and before a final adapter was written. No benchmark numbers are claimed for this checkpoint yet. Please treat it as experimental and evaluate it for your own workload.
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "groxaxo/MagiSeek-V1.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain what you can help me build."},
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Intended use
MagiSeek V1.1 is intended for research, experimentation, coding assistance, structured tool-use prototypes, and conversational applications where a model should turn context into a practical next step.
Do not use it as an unsupervised authority for medical, legal, financial, security, or other high-impact decisions. Generated code and tool arguments must be reviewed and sandboxed before execution. As an early checkpoint, it may be less stable than the base model on tasks outside its training emphasis.
Limitations and honest status
This is not a benchmark-certified release. The training run ended early, and the model card does not claim that V1.1 is better than MagiSeek-Pro-V1 on every task. It may overfit to coding-agent patterns, imitate incorrect tool calls, hallucinate facts, or produce confident but unsafe instructions. Always test with held-out prompts and keep human approval in the loop for external actions.
The training corpus was cleaned and structurally filtered locally. This card does not reproduce private source conversations or claim that every generated example is error-free. Users and downstream distributors remain responsible for their own data, safety, and license review.
License
The base model is published under Apache-2.0, and this merged release follows that license. See the base model card for upstream attribution and terms.
The short version
V1.1 is MagiSeek-Pro-V1 after its first serious field exercise: not the final destination, but a real, reproducible checkpoint from the climb.