Instructions to use agentic-ptb/grok-record with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Grok
How to use agentic-ptb/grok-record with Grok:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 8,900 Bytes
9b2f1cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | #!/usr/bin/env python3
"""Compact one-turn pi traces for LoRA on top of sft-v3. Generic skills, not eval items."""
from __future__ import annotations
import json
import random
from pathlib import Path
from datasets import Dataset, load_dataset
SYS = (
"You are a coding agent. Prefer tools over talk. "
"Chain work in one bash call. Never loop ls/cd/pwd. "
"After the required output exists, one short sentence and STOP."
)
TOOLS = json.dumps(
[
{
"type": "function",
"function": {
"name": "bash",
"description": "Execute a bash command",
"parameters": {
"type": "object",
"properties": {"command": {"type": "string"}},
"required": ["command"],
},
},
},
{
"type": "function",
"function": {
"name": "write",
"description": "Write a file",
"parameters": {
"type": "object",
"properties": {"path": {"type": "string"}, "content": {"type": "string"}},
"required": ["path", "content"],
},
},
},
{
"type": "function",
"function": {
"name": "edit",
"description": "Edit a file",
"parameters": {
"type": "object",
"properties": {"path": {"type": "string"}, "edits": {"type": "array"}},
"required": ["path", "edits"],
},
},
},
{
"type": "function",
"function": {
"name": "read",
"description": "Read a file",
"parameters": {
"type": "object",
"properties": {"path": {"type": "string"}},
"required": ["path"],
},
},
},
]
)
def tc(i, name, args):
return {"id": f"c{i}", "type": "function", "function": {"name": name, "arguments": json.dumps(args)}}
def pack(msgs, src):
return {"messages": [{"role": "system", "content": SYS}] + msgs, "tools": TOOLS, "source": src}
def main():
rng = random.Random(8)
rows = []
# extract archive to the destination named in the prompt (not a nested vendor dir)
for i in range(200):
rows.append(
pack(
[
{
"role": "user",
"content": (
f"A tarball is at /data/pkg{i}.tar.gz. Extract it to /workspace/pkg, "
"run `make -C /workspace/pkg && make -C /workspace/pkg install PREFIX=/output`, then stop."
),
},
{
"role": "assistant",
"content": "Extracting to /workspace/pkg and building.",
"tool_calls": [
tc(
0,
"bash",
{
"command": (
f"mkdir -p /workspace/pkg /output && "
f"tar -xzf /data/pkg{i}.tar.gz -C /workspace/pkg --strip-components=1 && "
"make -C /workspace/pkg && make -C /workspace/pkg install PREFIX=/output"
)
},
)
],
},
{"role": "tool", "tool_call_id": "c0", "content": "install done\n"},
{"role": "assistant", "content": "Built and installed to /output."},
],
"lora:extract",
)
)
# compact write via bash heredoc (fits in ~120 tokens)
for i in range(200):
rows.append(
pack(
[
{
"role": "user",
"content": (
"Write /output/strip.py that reads stdin and prints lines that do not contain "
"the substring TODO. Then stop."
),
},
{
"role": "assistant",
"content": "Writing the filter script.",
"tool_calls": [
tc(
0,
"bash",
{
"command": (
"cat > /output/strip.py << 'PY'\n"
"import sys\n"
"for line in sys.stdin:\n"
" if 'TODO' not in line:\n"
" sys.stdout.write(line)\n"
"PY\nchmod +x /output/strip.py"
)
},
)
],
},
{"role": "tool", "tool_call_id": "c0", "content": ""},
{"role": "assistant", "content": "Wrote /output/strip.py."},
],
"lora:heredoc",
)
)
# read then edit immediately (no second ls)
for i in range(180):
rows.append(
pack(
[
{"role": "user", "content": "off_by_one in /workspace/add.py: add(2,2) must be 4. Fix it and stop."},
{
"role": "assistant",
"content": "Reading then patching.",
"tool_calls": [tc(0, "bash", {"command": "cat /workspace/add.py"})],
},
{"role": "tool", "tool_call_id": "c0", "content": "def add(a, b):\n return a + b + 1\n"},
{
"role": "assistant",
"content": "Patching the return.",
"tool_calls": [
tc(
1,
"edit",
{
"path": "/workspace/add.py",
"edits": [{"oldText": " return a + b + 1\n", "newText": " return a + b\n"}],
},
)
],
},
{"role": "tool", "tool_call_id": "c1", "content": "Edited add.py"},
{"role": "assistant", "content": "add(2,2) is 4."},
],
"lora:edit",
)
)
# git restore previous commit (generic)
for i in range(120):
rows.append(
pack(
[
{
"role": "user",
"content": "The latest commit broke page.html. Restore that file from HEAD~1 and stop.",
},
{
"role": "assistant",
"content": "Restoring page.html from the previous commit.",
"tool_calls": [tc(0, "bash", {"command": "git checkout HEAD~1 -- page.html && git status --short"})],
},
{"role": "tool", "tool_call_id": "c0", "content": "M page.html\n"},
{"role": "assistant", "content": "Restored page.html from HEAD~1."},
],
"lora:git",
)
)
# mix original finish set so we keep stop-after-verify
prev = Path("/var/lib/agentptb-cache/tb1/tmp/sft-data/pi-sft-finish/train.parquet")
if prev.exists():
ds = load_dataset("parquet", data_files=str(prev), split="train")
idx = list(range(len(ds)))
rng.shuffle(idx)
for j in idx[:3500]:
rec = ds[int(j)]
rows.append({"messages": rec["messages"], "tools": rec["tools"], "source": rec["source"]})
rng.shuffle(rows)
out = Path("/var/lib/agentptb-cache/tb1/tmp/sft-data/pi-sft-lora")
out.mkdir(parents=True, exist_ok=True)
Dataset.from_dict(
{
"messages": [r["messages"] for r in rows],
"tools": [r["tools"] if isinstance(r["tools"], str) else json.dumps(r["tools"]) for r in rows],
"source": [r["source"] for r in rows],
}
).to_parquet(str(out / "train.parquet"))
print("wrote", len(rows), out)
if __name__ == "__main__":
main()
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