checkpoint-8000-biomimetic

xJoePec/checkpoint-8000-biomimetic is a Qwen3 causal language model checkpoint derived from xJoePec/checkpoint-8000. The Hub repository currently contains a complete model in sharded Safetensors format, rather than an adapter-only package.

The repository name identifies this experiment as biomimetic. The published files do not define a formal biomimicry objective or benchmark, so this card separates verified model facts from training details that are not published or independently reproducible from the Hub repository.

Model details

The parameter count and total stored weight size come from model.safetensors.index.json. The architecture and context settings come from config.json.

Configuration

The published configuration specifies:

• 36 decoder layers;
• hidden size 2,560;
• 32 attention heads and 8 key/value heads;
• head dimension 128;
• intermediate size 9,728;
• vocabulary size 151,936;
• RMSNorm epsilon 1e-6;
• RoPE theta 5,000,000.

The repository includes a Qwen3 chat template in chat_template.jinja. Use it through tokenizer.apply_chat_template instead of manually constructing role tokens. The published generation_config.json defaults to sampling with temperature=0.6, top_k=20, and top_p=0.95.

Training and provenance

Recorded Studio run

The local Unsloth Studio project whose name matches this repository contains an adapter artifact and trainer state. The following values are recorded in that artifact:

The step values above come from checkpoint-5000/trainer_state.json. The name checkpoint-8000 is not evidence that this run trained for 8,000 steps.

The local project artifact is adapter-only, while this Hub repository contains full model shards. The conversion or merge step is not included in the Hub file list, so the exact relationship between the final full-weight export and the local adapter checkpoint cannot be independently verified from Hub files. Load this repository as a complete model; do not assume that the adapter package can be reconstructed from it.

The local training record reports these environment versions:

• PEFT 0.18.1
• TRL 0.23.1
• Transformers 5.5.0
• PyTorch 2.10.0+cu130
• Datasets 4.3.0
• Tokenizers 0.22.2

The published model configuration also records Unsloth metadata: unsloth_version: 2026.7.6 and unsloth_fixed: true.

Not recorded for this follow-on run

The public Hub repository does not include a dataset manifest, dataset revision or license, exact base-model revision, full training arguments, held-out evaluation outputs, or training logs. Consequently, this card makes no claim about:

• the dataset used for this follow-on run;
• optimizer settings, learning-rate schedule, batch size, or random seed;
• hardware, wall-clock time, or training speed;
• capability, preference, factuality, safety, or alignment improvements.

The parent checkpoint's model card documents its own lineage and training data. Those details should not be attributed to this follow-on checkpoint without separate evidence.

Intended use

This checkpoint is intended for research and experimentation with Qwen3 text generation and conversational behavior. Compare it with the parent checkpoint under the same prompt set, chat template, decoding settings, and random seeds before drawing conclusions about the effect of this run.

This model is not a verified safety, factuality, medical, legal, financial, or general-purpose decision system. Do not use generated text as the sole basis for high-impact decisions.

Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "xJoePec/checkpoint-8000-biomimetic"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)

messages = [
    {"role": "user", "content": "Describe the purpose of this checkpoint."},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.6,
        top_k=20,
        top_p=0.95,
    )

new_tokens = outputs[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Lower-memory loading

The stored bfloat16 weights are approximately 8.04 GB before the KV cache and runtime overhead. On an 8 GB GPU, load in 4-bit precision or use CPU offload; a full bfloat16 load leaves little or no room for a useful KV cache.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "xJoePec/checkpoint-8000-biomimetic"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto",
)

Use a Transformers version with Qwen3 support and a compatible bitsandbytes installation for 4-bit loading. The configured 262,144-token context is a model limit, not a recommendation for an 8 GB device; input length, generated tokens, and KV-cache settings should be chosen for available memory.

Evaluation

No benchmark or held-out evaluation is published for this checkpoint, and the recorded trainer state contains no evaluation entries. A defensible comparison should:

1. use a held-out prompt set that was not used for training;
2. run the parent and this checkpoint with the same chat template, decoding parameters, and seed;
3. report the number and source of prompts and the exact generation settings; and
4. report task-specific metrics or blinded human judgments together with uncertainty and variation across seeds.

Do not interpret the repository name, training objective, or a change in style as evidence of improved reasoning, factuality, safety, or general helpfulness.

Limitations and responsible use

• The exact follow-on dataset and some training configuration are not recorded in the public repository.
• The model can hallucinate, reproduce biases from its base model or training data, and fail outside its training distribution.
• A conversational style change, if present, may trade off against factuality, calibration, diversity, or refusal quality; these properties require separate evaluation.
• The biomimetic label is an experiment name, not a validated scientific capability claim.
• Review upstream model and dataset terms before redistributing weights or outputs.

License and attribution

This repository declares the Apache-2.0 license in its Hub metadata. The base checkpoint and any training data may have additional provenance and license considerations; review their respective cards and terms before redistribution.

The model was prepared with the Transformers/Unsloth toolchain metadata present in the repository. This project is independent and is not affiliated with or endorsed by the authors of Qwen, Unsloth, or any upstream dataset.