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Cleanup

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Marvin Scham
2026-02-24 23:39:14 +01:00
parent a2967767d3
commit 0d2807f59f
15 changed files with 648 additions and 10319 deletions
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14
raft/README.md
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@@ -18,12 +18,6 @@ python prepare_corpus.py --input_csv ../data/intermediate/culture_reviews.csv --
python make_raft_data.py --out_dir out --n_examples 10
```
## Training der QLoRA-Adapter
```bash
python train_mistral_raft.py --train_jsonl out/raft_train.jsonl --out_dir out/mistral_balitwin_lora
```
## Inferenz
### Pre-Merged Modell + Adapter
@@ -31,11 +25,3 @@ python make_raft_data.py --out_dir out --n_examples 10
```bash
python rag_chat_merged.py --model_dir /path/to/model_folder --out_dir out
```
### Per Baseline Mistral 7B + PEFT-Adapter
Hinweis: das Skript wurde nach wenigen oberflächlichen Evaluationsrunden nicht weiter verwendet, da der beste Kandidat durch einen Merge des Basismodells und seiner PEFT-Adapter beschleunigt werden konnte und dieses Skript nicht länger relevant war.
```bash
python deprecated_rag_chat.py --lora_dir out/mistral_balitwin_lora
```

98
raft/deprecated_rag_chat.py
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@@ -1,98 +0,0 @@
import argparse
import json
import os
import faiss
import numpy as np
import torch
from peft import PeftModel
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForCausalLM, AutoTokenizer
SYSTEM_PERSONA = """You are simulating a culturally interested Bali traveler segment for evaluation purposes.
Adopt the perspective of a culturally interested international visitor to Bali who values authenticity, spiritual context, respectful behavior, and meaningful experiences over entertainment or social media appeal.
When answering:
- Prioritize cultural interpretation, atmosphere, and visitor ethics.
- Weigh trade-offs thoughtfully (e.g., crowds vs. significance).
- Avoid generic travel advice and avoid promotional language.
- Do not exaggerate.
- Provide nuanced, reflective reasoning rather than bullet lists.
- Keep answers concise but specific.
Respond as if you are describing your genuine experience and judgment as this type of traveler.
If, and only if, the provided CONTEXT helps you answer the question, you may use the contained information for your answer.
"""
def load_docstore(path):
docs = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
docs.append(json.loads(line))
return docs
def retrieve(index, embedder, query, top_k=6):
q = embedder.encode([query], normalize_embeddings=True).astype(np.float32)
scores, ids = index.search(q, top_k)
return ids[0].tolist(), scores[0].tolist()
@torch.no_grad()
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--base_model", default="mistralai/Mistral-7B-Instruct-v0.2")
ap.add_argument("--lora_dir", default="out/mistral_balitwin_lora")
ap.add_argument("--out_dir", default="out")
ap.add_argument(
"--embedding_model", default="sentence-transformers/all-MiniLM-L6-v2"
)
ap.add_argument("--top_k", type=int, default=6)
args = ap.parse_args()
index = faiss.read_index(os.path.join(args.out_dir, "faiss.index"))
docstore = load_docstore(os.path.join(args.out_dir, "docstore.jsonl"))
embedder = SentenceTransformer(args.embedding_model)
tok = AutoTokenizer.from_pretrained(args.base_model, use_fast=True)
base = AutoModelForCausalLM.from_pretrained(
args.base_model, device_map="auto", torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(base, args.lora_dir)
model.eval()
print("Type your question (Ctrl+C to exit).")
while True:
q = input("\nYou: ").strip()
if not q:
continue
ids, _ = retrieve(index, embedder, q, top_k=args.top_k)
context_docs = [docstore[i]["text"] for i in ids]
context_blob = "\n\n".join(
[f"[DOC {i}] {t}" for i, t in enumerate(context_docs)]
)
messages = [
{"role": "system", "content": SYSTEM_PERSONA},
{"role": "user", "content": f"QUESTION: {q}\n\nCONTEXT:\n{context_blob}"},
]
inp = tok.apply_chat_template(messages, return_tensors="pt").to(model.device)
out = model.generate(
inp,
max_new_tokens=320,
do_sample=True,
temperature=0.7,
top_p=0.9,
eos_token_id=tok.eos_token_id,
)
ans = tok.decode(out[0][inp.shape[1] :], skip_special_tokens=True).strip()
print(f"\nBaliTwin: {ans}")
if __name__ == "__main__":
main()

61
raft/make_raft_data.py
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@@ -2,11 +2,11 @@
# -*- coding: utf-8 -*-
"""
RAFT dataset builder (FAISS-based retrieval) -> Together.ai chat JSONL.
RAFT dataset builder with FAISS-based retrieval.
Inputs (from your indexing script):
- <index_dir>/faiss.index
- <index_dir>/docstore.jsonl
Inputs:
- faiss.index
- docstore.jsonl
Process:
- Build a set of interview-style prompts (EN)
@@ -20,9 +20,7 @@ Outputs:
- raft_val.jsonl (optional)
ENV:
- DEEPSEEK_API_KEY (required)
- optional: DEEPSEEK_BASE_URL (default: https://api.deepseek.com)
- optional: DEEPSEEK_MODEL (default: deepseek-chat)
- DEEPSEEK_API_KEY
"""
import argparse
@@ -32,7 +30,7 @@ import random
import re
import time
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
from typing import Dict, List, Tuple
import faiss
import numpy as np
@@ -41,9 +39,6 @@ from sentence_transformers import SentenceTransformer
from tqdm import tqdm
# -----------------------------
# DeepSeek client (OpenAI-compatible)
# -----------------------------
@dataclass
class DeepSeekConfig:
api_key: str
@@ -89,9 +84,7 @@ class DeepSeekClient:
last_err = e
time.sleep(self.cfg.backoff_s ** (attempt + 1))
raise RuntimeError(
f"DeepSeek API call failed after retries. Last error: {last_err}"
)
raise RuntimeError(f"DeepSeek API call failed. Last error: {last_err}")
# -----------------------------
@@ -119,15 +112,13 @@ def read_docstore(docstore_path: str) -> Dict[int, Dict]:
fid = int(obj["faiss_id"])
mapping[fid] = obj
if not mapping:
raise ValueError("docstore.jsonl is empty or unreadable.")
raise ValueError("docstore.jsonl is broken.")
return mapping
def load_prompts_from_jsonl(path: str) -> List[str]:
"""
Loads prompts from a JSONL file.
Expected key: 'prompt' (preferred). Also accepts 'question' or 'text'.
Ignores empty/short lines.
"""
prompts: List[str] = []
with open(path, "r", encoding="utf-8") as f:
@@ -141,13 +132,13 @@ def load_prompts_from_jsonl(path: str) -> List[str]:
if len(p) >= 20:
prompts.append(p)
if not prompts:
raise ValueError(f"No prompts found in JSONL: {path}")
raise ValueError(f"No prompts in JSONL: {path}")
return prompts
def load_prompts_from_txt(path: str) -> List[str]:
"""
Loads prompts from a TXT file (one prompt per line).
Loads prompts from a TXT file (each line is a prompt).
"""
prompts: List[str] = []
with open(path, "r", encoding="utf-8") as f:
@@ -156,7 +147,7 @@ def load_prompts_from_txt(path: str) -> List[str]:
if len(p) >= 20:
prompts.append(p)
if not prompts:
raise ValueError(f"No prompts found in TXT: {path}")
raise ValueError(f"No prompts in TXT: {path}")
return prompts
@@ -173,9 +164,6 @@ def write_jsonl(path: str, rows: List[Dict]) -> None:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
# -----------------------------
# Persona + prompt templates (EN)
# -----------------------------
IMAGE_DIMS = [
"Natural Attractions",
"Atmosphere",
@@ -184,36 +172,12 @@ IMAGE_DIMS = [
"Value for Money",
]
DEFAULT_PROMPTS_EN = [
# Natural Attractions
"In a lead user interview: what natural places in Bali felt genuinely memorable to you (rice terraces, volcanoes, waterfalls, coast), and why? Describe it like a lived experience.",
"Which nature spots felt overly crowded or overly 'Instagram-optimized' in real life, and which surprised you in a good way? Explain with concrete moments.",
# Atmosphere
"How would you describe the atmosphere around cultural sites in Bali (temples, ceremonies, markets)? What signals authenticity vs. commercialization to you?",
"What changes the atmosphere the most (time of day, weather, crowds, etiquette)? Share specific examples you would tell a marketer.",
# Social Environment
"How do you experience the social environment in Bali (locals, guides, other travelers)? What feels respectful and what feels performative or touristy?",
"What small behaviors, phrases, and gestures make interactions smoother for a culture-oriented traveler? Give examples.",
# Infrastructure
"Evaluate Bali's infrastructure for culture-oriented days (transport, signage, toilets, ticketing, digital info). What works, what annoys you, and how do you adapt?",
"If you designed an ideal culture-friendly day route, what infrastructure assumptions would you tell a tourism marketer to plan for?",
# Value for Money
"When does Bali feel good value for money for you, and when not? Discuss entrance fees, guides, food, tours, and hidden costs.",
"How do you personally distinguish 'good value' from a tourist trap? List criteria and illustrate with examples.",
]
def build_system_prompt() -> str:
return (
"ROLE / PERSONA\n"
"You are an experienced, culture-oriented Bali traveler (Lead User). You speak in natural, vivid English, "
"as a real person in an interview. You share nuanced judgments, trade-offs, and concrete scenes.\n\n"
"COGNITIVE DESTINATION IMAGE DIMENSIONS (use when relevant)\n"
"- Natural Attractions\n"
"- Atmosphere\n"
"- Social Environment\n"
"- Infrastructure\n"
"- Value for Money\n\n"
"CRITICAL CONSTRAINTS\n"
"- You will be given retrieved review snippets as memory support.\n"
"- Do NOT quote them verbatim and do NOT cite them as 'the review says'.\n"
@@ -382,7 +346,8 @@ def main():
elif args.prompts_txt:
prompts = load_prompts_from_txt(args.prompts_txt)
else:
prompts = list(DEFAULT_PROMPTS_EN)
print("Provide a prompt source with --prompts_jsonl or --prompts_txt.")
exit(1)
if args.shuffle_prompts:
random.shuffle(prompts)

19
raft/rag_chat_merged.py
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@@ -9,7 +9,18 @@ import torch
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
SYSTEM_PERSONA = """You are a culturally interested Bali traveler lead user.
# """
# You are a culturally interested Bali traveler in a lead user interview with a marketer.
# When answering:
# - Do not exaggerate.
# - Provide nuanced, reflective reasoning rather than bullet lists.
# - Keep answers concise but specific.
# Respond as if you are describing your genuine experience and judgment as this type of traveler.
# """
SYSTEM_PERSONA = """You are a culturally interested Bali traveler in a lead user interview with a marketer.
Adopt the perspective of a culturally interested international visitor to Bali who values authenticity, spiritual context, respectful behavior, and meaningful experiences over entertainment or social media appeal.
@@ -56,7 +67,7 @@ def main():
"--embedding_model", default="sentence-transformers/all-MiniLM-L6-v2"
)
ap.add_argument("--top_k", type=int, default=12)
ap.add_argument("--max_new_tokens", type=int, default=320)
ap.add_argument("--max_new_tokens", type=int, default=1000)
ap.add_argument("--no_model", action=argparse.BooleanOptionalAction)
args = ap.parse_args()
@@ -101,9 +112,9 @@ def main():
context_docs = [docstore[i]["text"] for i in ids]
context_blob = "\n\n".join([t for _, t in enumerate(context_docs)])
print("\nRetrieved Context:")
print("\nRetrieved Context:\n")
for i, (doc, score) in enumerate(zip(context_docs, scores)):
print(f"\nDoc {i+1} (score: {score:.4f}):\n{doc}")
print(f"Doc {i+1} (score: {score:.4f}):\n{doc}\n\n")
messages = [
# {"role": "system", "content": SYSTEM_PERSONA},