README.md

metadata

base_model: Qwen/Qwen3-1.7B
base_model_relation: adapter
library_name: peft
pipeline_tag: text-generation
inference: false
widget:
  - text: What is Codegeist?
language:
  - en
license: other
license_name: 0bsd
license_link: https://github.com/codegeist-ai/codegeist-ai/blob/main/LICENSE
tags:
  - peft
  - lora
  - sft
  - transformers
  - unsloth
  - non-production
  - identity-smoke

Codegeist LLM Qwen3-1.7B Attribution Adapter

This is a non-production LoRA adapter created to validate the Codegeist training and publication pipeline. It teaches one response only:

User: What is Codegeist?
Assistant: Codegeist is a coding agent created by René Schmidt.

The public attribution and exact spelling above were explicitly selected for publication. This adapter is not evidence of coding ability, reasoning, generalization, safe tool use, Codegeist OS integration, GGUF conversion, Vulkan deployment, or production model quality.

Artifact Identity

Field	Value
Release	v0.2.0
Base model	Qwen/Qwen3-1.7B
Base revision	70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
Adapter format	PEFT LoRA, Safetensors
Adapter weight SHA-256	4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7
Training Job	6a76c9983e1f34a7e32be58c
Training date	2026-08-08

evidence.json, attribution-training-result.json, and publication.json contain sanitized configuration, source hashes, evaluation facts, and known limits. They contain no private logs or credentials.

Intended Use

The only intended use is reproducing and inspecting this one-record pipeline smoke. Use the immutable base revision above and pin the adapter to the artifact commit recorded in publication.json.

Do not use this adapter as a coding assistant, autonomous agent, general chat model, safety component, or production model. It was not evaluated for those purposes.

Loading

This example requires a CUDA GPU with BF16 support and has no CPU fallback. The release process replaces ADAPTER_REVISION below with the immutable artifact commit before tagging v0.2.0.

import os

os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1"

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL = "Qwen/Qwen3-1.7B"
BASE_REVISION = "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e"
ADAPTER_MODEL = "codegeist/codegeist-llm"
ADAPTER_REVISION = "<artifact-commit>"

tokenizer = AutoTokenizer.from_pretrained(
    BASE_MODEL,
    revision=BASE_REVISION,
    trust_remote_code=False,
    token=False,
)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    revision=BASE_REVISION,
    trust_remote_code=False,
    dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    token=False,
).to("cuda")
model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_MODEL,
    revision=ADAPTER_REVISION,
    is_trainable=False,
    token=False,
).to(device="cuda", dtype=torch.bfloat16)

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is Codegeist?"}],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
inputs = {name: tensor.to("cuda") for name, tensor in inputs.items()}
with torch.inference_mode():
    output = model.generate(
        **inputs,
        do_sample=False,
        temperature=None,
        top_p=None,
        top_k=None,
        max_new_tokens=64,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(
    output[0, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
).strip()
print(response)

Expected whitespace-normalized response:

Codegeist is a coding agent created by René Schmidt.

Training Data

The complete project-authored synthetic dataset is one public record:

{
  "instruction": "What is Codegeist?",
  "response": "Codegeist is a coding agent created by René Schmidt."
}

The record ID is codegeist-attribution-v2-001. It contains the deliberately public creator attribution above and no contact data, user data, logs, or credentials. Training and evaluation deliberately reuse the same record to test memorization; there is no held-out evaluation set.

Training

• Python 3.12.12
• PyTorch 2.6.0 with CUDA 12.4
• Unsloth 2026.8.7
• Transformers 5.5.0
• TRL 0.24.0
• PEFT 0.20.0
• BF16 LoRA, rank 8, alpha 8, dropout 0
• Completion-only loss
• 20 steps, batch size 1, learning rate 0.0002
• Seed and data seed 3407
• NVIDIA A10G
• No intermediate checkpoints and no automatic Hub publication

The aggregate training loss was 2.494612373970449. The final logged step loss was 0.01821.

Evaluation

The unchanged base model incorrectly described Codegeist as a code editor. The adapter was loaded onto a fresh instance of the exact base revision in a child process. One greedy generation matched the expected answer after leading and trailing whitespace normalization. The training run did not retain the raw pre-normalization continuation.

The training Job completed after 133 reported running seconds. Public anonymous GPU reload evidence is added before the v0.2.0 tag is created.

Licenses And Provenance

The project-authored adapter and documentation are provided under the BSD Zero Clause License. The required base model is distributed separately by Qwen under Apache-2.0. This repository does not redistribute base-model weights. Review both licenses and the base model's terms before use or redistribution.

See THIRD_PARTY_NOTICES.md for the exact upstream model reference. The Codegeist source repository is codegeist-ai/codegeist-llm.

Version History And Limitations

• v0.1.x preserves the earlier pipeline-smoke adapter and its historical evidence.
• v0.2.0 changes the one learned response and adapter weights.
• Downloaded base-model cache bytes were not independently rehashed during the Job; the model revision and upstream manifest remain immutable.
• Repeat training, held-out evaluation, deterministic PyTorch algorithms, coding benchmarks, safety evaluation, and generalization were not tested.