Ship Assistent 0.13.0: real QLoRA pipeline and GGUF Ollama register.

Replace fake train loop with TRL SFTTrainer, HF column mapping with fiction preset, safetensors to GGUF conversion, and ollama create using ollama_base plus ADAPTER. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-22 14:45:07 +03:00
parent 1a03c3178f
commit 474a674a35
12 changed files with 1002 additions and 133 deletions
@@ -0,0 +1,94 @@
+"""Map Hugging Face dataset rows to chat messages for SFT."""
+from __future__ import annotations
+
+
+def fiction_tags_text_user(row: dict, title_col: str = "title", tags_col: str = "tags") -> str:
+    parts = []
+    title = (row.get(title_col) or "").strip()
+    tags = (row.get(tags_col) or "").strip()
+    if title:
+        parts.append(f"Title: {title}")
+    if tags:
+        parts.append(f"Tags: {tags}")
+    return "\n".join(parts) if parts else tags or title or ""
+
+
+def row_to_messages(row: dict, schema: dict | None, mapping: dict | None) -> list[dict] | None:
+    schema = schema or {}
+    mapping = mapping or {}
+    kind = schema.get("kind") or mapping.get("kind") or mapping.get("preset")
+
+    if kind == "messages" and row.get("messages"):
+        return _normalize_messages(row["messages"])
+
+    if kind == "conversations" and row.get("conversations"):
+        out = []
+        for c in row["conversations"]:
+            if not isinstance(c, dict):
+                continue
+            frm = c.get("from") or ""
+            val = c.get("value") or ""
+            role = "assistant" if frm in ("gpt", "assistant", "chatgpt") else "user"
+            if frm in ("human", "user"):
+                role = "user"
+            if val:
+                out.append({"role": role, "content": str(val)})
+        return out or None
+
+    if kind == "alpaca":
+        instr = (row.get("instruction") or "").strip()
+        inp = (row.get("input") or "").strip()
+        output = (row.get("output") or "").strip()
+        user = instr if not inp else f"{instr}\n{inp}"
+        if user and output:
+            return [{"role": "user", "content": user}, {"role": "assistant", "content": output}]
+        return None
+
+    if kind == "prompt_response":
+        resp_col = schema.get("response_col") or "response"
+        prompt = row.get("prompt") or ""
+        resp = row.get(resp_col) or ""
+        if prompt and resp:
+            return [{"role": "user", "content": str(prompt)}, {"role": "assistant", "content": str(resp)}]
+        return None
+
+    if kind == "qa":
+        q = row.get("question") or ""
+        a = row.get("answer") or ""
+        if q and a:
+            return [{"role": "user", "content": str(q)}, {"role": "assistant", "content": str(a)}]
+        return None
+
+    if kind in ("fiction_tags_text", "preset_fiction_tags_text"):
+        text = (row.get("text") or "").strip()
+        user = fiction_tags_text_user(row)
+        if user and text:
+            return [{"role": "user", "content": user}, {"role": "assistant", "content": text}]
+        return None
+
+    if kind == "custom" or mapping.get("user_col"):
+        user_col = mapping.get("user_col")
+        asst_col = mapping.get("assistant_col")
+        if user_col and asst_col:
+            u = row.get(user_col) or ""
+            a = row.get(asst_col) or ""
+            if u and a:
+                return [{"role": "user", "content": str(u)}, {"role": "assistant", "content": str(a)}]
+        return None
+
+    if row.get("messages"):
+        return _normalize_messages(row["messages"])
+
+    return None
+
+
+def _normalize_messages(msgs) -> list[dict] | None:
+    out = []
+    for m in msgs:
+        if not isinstance(m, dict):
+            continue
+        role = m.get("role") or "user"
+        content = m.get("content") or m.get("text") or ""
+        if content:
+            out.append({"role": role, "content": str(content)})
+    return out or None