README.md

---
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 Qwen3-1.7B Identity Smoke Adapter

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

```text
User: What is Codegeist?
Assistant: Codegeist is a coding agent.
```

It 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 |
| --- | --- |
| Base model | `Qwen/Qwen3-1.7B` |
| Base revision | `70d244cc86ccca08cf5af4e1e306ecf908b1ad5e` |
| Adapter format | PEFT LoRA, Safetensors |
| Adapter weight SHA-256 | `19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8` |
| Training Job | `6a75f25a3e1f34a7e32bd646` |
| Training date | 2026-08-07 |

`evidence.json` contains the sanitized run chronology, configuration, package
versions, hashes, cost estimate, verification status, and known gaps. It does
not contain model weights, 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 this adapter repository to
a specific Hub commit when loading it.

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.
Replace `ADAPTER_REVISION` with an immutable commit from this repository:

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

BASE_MODEL = "Qwen/Qwen3-1.7B"
BASE_REVISION = "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e"
ADAPTER_MODEL = "codegeist/qwen3-1.7b-codegeist-identity-smoke"
ADAPTER_REVISION = "04d51edac56c6f1e068c644bfa8d014cadcecf9f"

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

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:

```text
Codegeist is a coding agent.
```

## Training Data

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

```json
{
  "instruction": "What is Codegeist?",
  "response": "Codegeist is a coding agent."
}
```

The record ID is `codegeist-identity-v1-001`. It contains no private data,
personal information, or credentials. Training and evaluation deliberately use
the same record to test memorization; there is no held-out evaluation set.

## Training

- Python 3.12
- 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 `1.6867698234826094`. The final logged step loss
was approximately `0.0003`.

## Evaluation

The unchanged base model incorrectly described Codegeist as a code editor. After
training, the adapter was loaded onto a fresh instance of the exact base revision
in a child process. One greedy generation produced the expected answer after
leading and trailing whitespace normalization.

The raw decoded continuation before `.strip()` was not retained. Training and
inference repeatability, deterministic PyTorch algorithms, coding benchmarks,
safety evaluation, and generalization were not tested.

The publication test uses the immutable public adapter commit on NVIDIA A10G
with CUDA, BF16, and full parameter offload. CPU inference is outside the
supported contract.

## Licenses And Provenance

The project-authored adapter and documentation are provided under the
[BSD Zero Clause License](https://github.com/codegeist-ai/codegeist-ai/blob/main/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`](https://github.com/codegeist-ai/codegeist-llm).

## Publication Limitations

- The successful training source was not committed when the paid Job launched;
  exact source bytes are anchored by SHA-256 in `evidence.json`.
- Downloaded model and tokenizer cache bytes were not independently rehashed
  inside the Job against the upstream manifest.
- The generated adapter configuration originally omitted the base revision; the
  publication copy sets it to the immutable revision used by the Job.
- This publication does not change the experiment's non-production status.