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txmedai
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mistralai/Mistral-7B-Instruct-v0.3
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Supported Functionality
| revision | what it is |
|---|---|
main / v3 | Latest. r=32, α=64, full attn + MLP (q/k/v/o, gate/up/down). Refined dataset and hparams over v2. |
v2 | Same target-module set as v3, earlier dataset iteration. |
v1 | First public release. r=32, α=64, attn + MLP minus down_proj. The only version with a published Pangram-3.2 eval (below). Same task and eval methodology apply to v2 and v3. |
Load a specific version by passing revision="v1" (or "v2", "v3") to
PeftModel.from_pretrained.
48-item confidently-AI eval set, scored against Pangram v3:
| metric | value |
|---|---|
| score_strict (bypass × sim, sim<0.5 outputs counted as not-bypassed) | 0.576 |
| bypass_rate (Pangram fraction_human > 0.5) | 93.6% (45/48) |
| bypass_strict (bypass + sim≥0.5) | 85.1% (41/48) |
| mean_semantic_sim | 0.677 |
| n_drift_collapsed (sim < 0.5) | 4/48 |
v2 / v3 evals on the same set have not yet been published here.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel base_id = "mistralai/Mistral-7B-Instruct-v0.3"adapter_id = "txmedai/humanizer-mistral7b-lora" # main = v3# adapter_id, revision="v1" # to pin to a previous version bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) tok = AutoTokenizer.from_pretrained(base_id)base = AutoModelForCausalLM.from_pretrained(base_id, quantization_config=bnb, device_map="auto", torch_dtype=torch.bfloat16)model = PeftModel.from_pretrained(base, adapter_id)model.eval() SYSTEM = ("Rewrite the text below in a casual, conversational human voice. " "Use contractions. Vary sentence length sharply. Drop academic phrasing. " "Keep all the meaning — same facts, same numbers — but completely change the surface style.") def humanize(text, temperature=0.9, top_p=0.95, max_new_tokens=400): msgs = [{"role": "user", "content": f"{SYSTEM}\n\n{text}"}] prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) ids = tok(prompt, return_tensors="pt", truncation=True, max_length=512).to(model.device) out = model.generate(**ids, max_new_tokens=max_new_tokens, min_new_tokens=60, temperature=temperature, top_p=top_p, repetition_penalty=1.15, do_sample=True, pad_token_id=tok.eos_token_id) return tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True).strip()Runs in ~6 GB VRAM in 4-bit, or via Colab / Modal / RunPod.
down_proj)Research and personal-use rewriting of AI-generated text into a conversational register, including evaluating detector robustness. Not intended to deceive academic, journalistic, or legal evaluators about the origin of text.
Apache-2.0 for the adapter weights. The base model
mistralai/Mistral-7B-Instruct-v0.3 is governed by Mistral's own license —
review it before redistributing merged weights.
This repository consolidates three previously-separate repos
(humanizer-mistral7b-lora-v1, -v2, -v3). Older repos have been retired
in favor of the revision= mechanism above.
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel base_id = "mistralai/Mistral-7B-Instruct-v0.3"adapter_id = "txmedai/humanizer-mistral7b-lora" # main = v3# adapter_id, revision="v1" # to pin to a previous version bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) tok = AutoTokenizer.from_pretrained(base_id)base = AutoModelForCausalLM.from_pretrained(base_id, quantization_config=bnb, device_map="auto", torch_dtype=torch.bfloat16)model = PeftModel.from_pretrained(base, adapter_id)model.eval() SYSTEM = ("Rewrite the text below in a casual, conversational human voice. " "Use contractions. Vary sentence length sharply. Drop academic phrasing. " "Keep all the meaning — same facts, same numbers — but completely change the surface style.") def humanize(text, temperature=0.9, top_p=0.95, max_new_tokens=400): msgs = [{"role": "user", "content": f"{SYSTEM}\n\n{text}"}] prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) ids = tok(prompt, return_tensors="pt", truncation=True, max_length=512).to(model.device) out = model.generate(**ids, max_new_tokens=max_new_tokens, min_new_tokens=60, temperature=temperature, top_p=top_p, repetition_penalty=1.15, do_sample=True, pad_token_id=tok.eos_token_id) return tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True).strip()