Skip to content
Studio GGUF CI

Fix Studio custom folders on Linux external drives (#6799) #9584

Sign in to view logs

Fix Studio custom folders on Linux external drives (#6799)

Fix Studio custom folders on Linux external drives (#6799) #9584

Workflow file for this run

.github/workflows/studio-inference-smoke.yml at 01f7e14
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# Three end-to-end smoke jobs that boot a freshly-installed Studio and
# exercise the surfaces real users hit through the OpenAI / Anthropic
# SDKs and curl. Each job picks the smallest model that exercises the
# behaviour under test, primes HF_HOME via actions/cache, and shares
# the install.sh --local --no-torch bootstrap.
#
# 1. OpenAI, Anthropic API tests
# gemma-3-270m-it UD-Q4_K_XL (~254 MiB).
# Password rotation via /api/auth/change-password (old fails,
# new works), then OpenAI + Anthropic Python SDKs against /v1/*
# with temperature=0 and a fixed seed. Asserts the four-turn
# conversation is deterministic across two runs.
#
# 2. Tool calling Tests
# Qwen3.5-2B UD-IQ3_XXS (~890 MiB). OpenAI function calling,
# server-side tools (python, terminal, web_search) via
# enable_tools / enabled_tools, and enable_thinking on/off.
#
# 3. JSON, images
# Qwen3-VL-2B-Instruct UD-Q4_K_XL (~1.1 GiB) + mmproj-F16 (~780 MiB).
# response_format JSON-schema decoding and OpenAI image_url
# (data URI) plus Anthropic source/base64 image inputs.
#
# All three jobs run in parallel. Total wall time is dominated by job 3
# on a cold cache; warm cache cuts that to ~3 min.
name: Studio GGUF CI
on:
pull_request:
paths:
- 'studio/**'
- 'unsloth/**'
- 'unsloth_cli/**'
- 'install.sh'
- 'pyproject.toml'
- '.github/workflows/studio-inference-smoke.yml'
push:
branches: [main, pip]
# Manual trigger for pre-warming HF_HOME caches on main, or re-running
# against an arbitrary branch without pushing a no-op commit.
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
permissions:
contents: read
jobs:
# ─────────────────────────────────────────────────────────────────────
# Job 1: OpenAI, Anthropic API tests
# ─────────────────────────────────────────────────────────────────────
openai-anthropic:
name: OpenAI, Anthropic API tests
runs-on: ubuntu-latest
timeout-minutes: 25
env:
GGUF_REPO: unsloth/gemma-3-270m-it-GGUF
GGUF_VARIANT: UD-Q4_K_XL
GGUF_FILE: gemma-3-270m-it-UD-Q4_K_XL.gguf
STUDIO_PORT: '18888'
HF_HOME: ${{ github.workspace }}/hf-cache
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Linux deps for llama.cpp prebuilt
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
libcurl4-openssl-dev libssl-dev jq
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: '22'
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: '3.12'
cache: 'pip'
- name: Restore HF_HOME for ${{ env.GGUF_REPO }}
id: cache-hf
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
continue-on-error: true
with:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Prime HF_HOME with the GGUF
id: prime-hf
if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
env:
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
run: |
python -m pip install --upgrade huggingface_hub
mkdir -p hf-cache
bash .github/scripts/hf-download-with-retry.sh "$GGUF_REPO" "$GGUF_FILE"
bash .github/scripts/hf-download-with-retry.sh ggml-org/models tinyllamas/stories260K.gguf
- name: Save HF_HOME for ${{ env.GGUF_REPO }}
if: always() && steps.prime-hf.outcome == 'success'
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
run: |
mkdir -p logs
set -o pipefail
bash install.sh --local --no-torch 2>&1 | tee logs/install.log
- name: Install OpenAI + Anthropic Python SDKs
run: pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p "$STUDIO_PORT" \
> logs/studio.log 2>&1 &
echo "STUDIO_PID=$!" >> "$GITHUB_ENV"
- name: Wait for /api/health
run: |
for i in $(seq 1 180); do
if curl -fs "http://127.0.0.1:${STUDIO_PORT}/api/health" > /tmp/health.json; then
jq -e '.status == "healthy"' /tmp/health.json
exit 0
fi
sleep 1
done
echo "Studio did not become healthy in 180s"
tail -200 logs/studio.log
exit 1
- name: Password rotation (old must fail, new must work)
run: |
OLD=$(cat ~/.unsloth/studio/auth/.bootstrap_password)
NEW="CIRotated-$(python -c 'import secrets; print(secrets.token_urlsafe(12))')"
echo "::add-mask::$OLD"
echo "::add-mask::$NEW"
# 1. Login with the bootstrap password.
OLD_TOKEN=$(curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$OLD\"}" | jq -r .access_token)
[ -n "$OLD_TOKEN" ] && [ "$OLD_TOKEN" != "null" ] || { echo "bootstrap login failed"; exit 1; }
# 2. Rotate to a fresh random password.
curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/change-password" \
-H "Authorization: Bearer $OLD_TOKEN" -H 'content-type: application/json' \
-d "{\"current_password\":\"$OLD\",\"new_password\":\"$NEW\"}" > /dev/null
# 3. Old password must now be rejected (HTTP 401).
OLD_STATUS=$(curl -s -o /dev/null -w '%{http_code}' \
-X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$OLD\"}")
if [ "$OLD_STATUS" != "401" ]; then
echo "::error::Login with old password returned $OLD_STATUS, expected 401"
exit 1
fi
# 4. New password must succeed; capture the JWT for downstream steps.
NEW_TOKEN=$(curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$NEW\"}" | jq -r .access_token)
[ -n "$NEW_TOKEN" ] && [ "$NEW_TOKEN" != "null" ] || { echo "new login failed"; exit 1; }
echo "TOKEN=$NEW_TOKEN" >> "$GITHUB_ENV"
echo "password rotation OK (old=401, new=200)"
- name: Load the GGUF (HF repo + variant, served from HF_HOME cache)
run: |
curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/inference/load" \
-H "Authorization: Bearer $TOKEN" -H 'content-type: application/json' \
--max-time 600 \
-d "{\"model_path\":\"$GGUF_REPO\",\"gguf_variant\":\"$GGUF_VARIANT\",\"is_lora\":false,\"max_seq_length\":2048}" \
| jq '{status, display_name, is_gguf, context_length}'
- name: Multi-turn determinism via OpenAI + Anthropic SDKs
env:
BASE_URL: http://127.0.0.1:18888
run: |
python - <<'PY'
import json
import os
from openai import OpenAI
from anthropic import Anthropic
BASE = os.environ["BASE_URL"]
KEY = os.environ["TOKEN"] # JWT also accepted as Bearer on /v1/*
SEED = 3407
# Four-turn conversation: the second and fourth turns can only be
# answered correctly if the model sees the prior turns, so this
# also exercises the conversation-history wiring.
PROMPTS = [
"What is 1+1?",
"What did I ask before?",
"What is the capital of France?",
"Repeat the city name",
]
def run_openai():
client = OpenAI(base_url = f"{BASE}/v1", api_key = KEY)
history, replies = [], []
for prompt in PROMPTS:
history.append({"role": "user", "content": prompt})
resp = client.chat.completions.create(
model = "default",
messages = history,
temperature = 0.0,
max_tokens = 80,
seed = SEED,
extra_body = {"enable_thinking": False},
)
text = resp.choices[0].message.content or ""
replies.append(text)
history.append({"role": "assistant", "content": text})
return replies
def run_anthropic():
# Two SDK quirks vs. Studio:
# 1. base_url must NOT include /v1 -- the SDK appends
# /v1/messages itself; otherwise the request hits
# /v1/v1/messages and 405s.
# 2. The SDK sends `x-api-key` by default, but Studio's
# auth layer is HTTPBearer-only. Override via
# default_headers so Authorization: Bearer ... is
# sent instead.
client = Anthropic(
base_url = BASE,
api_key = "unused",
default_headers = {"Authorization": f"Bearer {KEY}"},
)
history, replies = [], []
for prompt in PROMPTS:
history.append({"role": "user", "content": prompt})
msg = client.messages.create(
model = "default",
max_tokens = 80,
messages = history,
temperature = 0.0,
extra_body = {"seed": SEED, "enable_thinking": False},
)
text = "".join(b.text for b in msg.content if getattr(b, "type", None) == "text")
replies.append(text)
history.append({"role": "assistant", "content": text})
return replies
for label, runner in (("openai", run_openai), ("anthropic", run_anthropic)):
first = runner()
second = runner()
determinism_failures = []
for i, (a, b) in enumerate(zip(first, second), start = 1):
print(f"[{label} turn {i}] {a!r}")
# Both runs must be non-empty; small-quant drift
# across runs is WARN-only (grounding asserts below
# are the stronger signal).
assert a, f"{label}: empty turn {i} response in first run"
assert b, f"{label}: empty turn {i} response in second run"
if a.strip() != b.strip():
determinism_failures.append(
f"turn {i}: run1={a!r} run2={b!r}"
)
if determinism_failures:
print(
f"[{label}] WARN non-determinism at temperature=0.0 across "
f"{len(determinism_failures)} of {len(first)} turn(s); "
f"small-quant model drift, not a Studio regression. "
f"Details: " + " | ".join(determinism_failures)
)
# Sanity: turn-2 reply should mention the earlier question, and
# turn-4 reply should mention Paris (model echoes the city it
# produced for turn 3). Lower-cased substring checks keep the
# assertion robust to formatting jitter.
joined = " ".join(first).lower()
assert "1" in first[0], f"{label}: turn-1 answer should contain '1', got {first[0]!r}"
assert "paris" in joined, f"{label}: expected 'paris' somewhere in the four-turn transcript: {first}"
status_word = "PASS" if not determinism_failures else "PASS (with drift)"
print(f"[{label}] {status_word} -- 4 turns, history grounded ('paris' present)")
PY
- name: Stop Studio
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
ss -tln | grep ":${STUDIO_PORT}" || true
- name: Upload logs
# Always upload so green runs are still reviewable.
if: always()
# Diagnostic only: a transient artifact-service drop must not fail a green job.
continue-on-error: true
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: openai-anthropic-log
path: |
logs/studio.log
logs/install.log
retention-days: 7
# ─────────────────────────────────────────────────────────────────────
# Job 2: Tool calling Tests
# ─────────────────────────────────────────────────────────────────────
tool-calling:
name: Tool calling Tests
runs-on: ubuntu-latest
timeout-minutes: 25
env:
# Tool calling is the highest-volume GGUF in this workflow
# (Qwen3.5-2B at Q4_K_XL = ~1.28 GiB). Caching HF_HOME would
# store xet chunks + blobs + snapshots = ~4 GiB compressed --
# 4-5x file-size inflation, dominated by xet chunks. Use main's
# `--local-dir gguf-cache` pattern to cache the flat .gguf only.
# Studio's /api/inference/load accepts either a HF repo (which
# uses HF_HOME) or an absolute file path; passing the absolute
# path keeps the test off HF_HOME entirely so the cache size
# tracks the GGUF file 1:1. The OpenAI/Anth and JSON+images
# jobs still cover the gguf_variant resolution path.
# Q4_K_XL, not IQ3_XXS: at IQ3_XXS this model emits malformed
# tool calls that llama-server's peg-native parser rejects with a
# 500. Mac/Windows already use Q4_K_XL for the same reason.
GGUF_REPO: unsloth/Qwen3.5-2B-GGUF
GGUF_FILE: Qwen3.5-2B-UD-Q4_K_XL.gguf
STUDIO_PORT: '18889'
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Linux deps for llama.cpp prebuilt
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
libcurl4-openssl-dev libssl-dev jq
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: '22'
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: '3.12'
cache: 'pip'
- name: Restore GGUF model file
id: cache-gguf
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
continue-on-error: true
with:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Download GGUF if cache miss
id: download-gguf
if: steps.cache-gguf.outputs.cache-hit != 'true' || steps.cache-gguf.outcome != 'success'
env:
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
run: |
python -m pip install --upgrade huggingface_hub
mkdir -p gguf-cache
bash .github/scripts/hf-download-with-retry.sh "$GGUF_REPO" "$GGUF_FILE" gguf-cache
- name: Save GGUF model file
if: always() && steps.download-gguf.outcome == 'success'
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Install Studio (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
run: |
mkdir -p logs
set -o pipefail
bash install.sh --local --no-torch 2>&1 | tee logs/install.log
- name: Reset auth + boot Studio (API-only, default tool policy)
# We deliberately use the API-only mode rather than
# `unsloth studio run` because the latter calls
# `set_tool_policy(...)` with a resolved bool: on loopback the
# default resolves to True, which forces every request through
# the server-side agentic loop and breaks the standard
# function-calling test below. API-only mode leaves
# tool_policy=None so each request's `enable_tools` field is
# honoured.
run: |
unsloth studio reset-password
mkdir -p logs
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p "$STUDIO_PORT" \
> logs/studio.log 2>&1 &
echo "STUDIO_PID=$!" >> "$GITHUB_ENV"
- name: Wait for /api/health, log in, change password, load model
run: |
for i in $(seq 1 180); do
if curl -fs "http://127.0.0.1:${STUDIO_PORT}/api/health" > /tmp/health.json; then
jq -e '.status == "healthy"' /tmp/health.json && break
fi
sleep 1
done
jq -e '.status == "healthy"' /tmp/health.json
OLD=$(cat ~/.unsloth/studio/auth/.bootstrap_password)
NEW="CITool-$(python -c 'import secrets; print(secrets.token_urlsafe(12))')"
echo "::add-mask::$OLD"
echo "::add-mask::$NEW"
OLD_TOKEN=$(curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$OLD\"}" | jq -r .access_token)
curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/change-password" \
-H "Authorization: Bearer $OLD_TOKEN" -H 'content-type: application/json' \
-d "{\"current_password\":\"$OLD\",\"new_password\":\"$NEW\"}" > /dev/null
TOKEN=$(curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$NEW\"}" | jq -r .access_token)
echo "API_KEY=$TOKEN" >> "$GITHUB_ENV"
GGUF_PATH="$GITHUB_WORKSPACE/gguf-cache/${GGUF_FILE}"
ls -lh "$GGUF_PATH"
curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/inference/load" \
-H "Authorization: Bearer $TOKEN" -H 'content-type: application/json' \
--max-time 600 \
-d "{\"model_path\":\"$GGUF_PATH\",\"is_lora\":false,\"max_seq_length\":2048}" \
| jq '{status, display_name}'
- name: Tool calling, server-side tools, thinking on/off
env:
BASE_URL: http://127.0.0.1:18889
run: |
python - <<'PY'
import json
import os
import urllib.request
BASE = os.environ["BASE_URL"]
KEY = os.environ["API_KEY"]
SEED = 3407
def post(path, body, *, timeout = 240):
"""Plain JSON POST. For requests that don't go through
the server-side agentic loop, the response is one JSON
object."""
data = json.dumps(body).encode()
req = urllib.request.Request(
f"{BASE}{path}",
data = data,
method = "POST",
headers = {
"Authorization": f"Bearer {KEY}",
"Content-Type": "application/json",
},
)
with urllib.request.urlopen(req, timeout = timeout) as resp:
return resp.status, json.loads(resp.read().decode())
def post_sse(path, body, *, timeout = 600):
"""POST a streaming request and accumulate the assistant
text deltas. The server-side agentic loop ALWAYS returns
SSE regardless of the request's `stream` field, so any
call with enable_tools=true must use this helper.
Returns (content, raw_payloads):
content -- concatenated assistant delta.content
raw_payloads -- list of every raw "data: ..." event
payload (JSON strings). Callers asserting
that a server-side tool actually ran (and
not just that the model emitted some
text) should grep raw_payloads for tool
invocation markers / tool output, since
`delta.content` alone is not evidence
that the tool path executed.
"""
body = {**body, "stream": True}
data = json.dumps(body).encode()
req = urllib.request.Request(
f"{BASE}{path}",
data = data,
method = "POST",
headers = {
"Authorization": f"Bearer {KEY}",
"Content-Type": "application/json",
},
)
parts = []
events = []
with urllib.request.urlopen(req, timeout = timeout) as resp:
for raw in resp:
line = raw.decode().strip()
if not line.startswith("data: "):
continue
payload = line[6:]
if payload == "[DONE]":
break
events.append(payload)
try:
chunk = json.loads(payload)
except json.JSONDecodeError:
continue
for choice in chunk.get("choices", []):
delta = choice.get("delta", {}) or {}
if delta.get("content"):
parts.append(delta["content"])
return "".join(parts), events
_STUDIO_TOOL_TYPES = {
"tool_start", "tool_end", "tool_use", "tool_result",
}
def _tool_invoked(events):
"""Structural check: True iff some SSE payload is a real
tool envelope (Studio tool_start/tool_end, Anthropic
tool_use/tool_result, OpenAI non-empty delta.tool_calls /
message.tool_calls / finish_reason='tool_calls' /
role:'tool' / function_call). tool_status is NOT
evidence: Studio emits empty tool_status events on
iteration boundaries even when no tool ran.
"""
for raw in events:
try:
ev = json.loads(raw)
except (json.JSONDecodeError, TypeError):
continue
if not isinstance(ev, dict):
continue
if ev.get("type") in _STUDIO_TOOL_TYPES:
return True
for choice in ev.get("choices", []) or []:
if not isinstance(choice, dict):
continue
if choice.get("finish_reason") == "tool_calls":
return True
for src_key in ("delta", "message"):
src = choice.get(src_key) or {}
if not isinstance(src, dict):
continue
tc = src.get("tool_calls")
if isinstance(tc, list) and tc:
return True
if src.get("function_call"):
return True
if src.get("role") == "tool":
return True
for item in ev.get("output", []) or []:
if isinstance(item, dict) and item.get("type") in {
"tool_call", "function_call", "tool_use",
}:
return True
content = ev.get("content")
if isinstance(content, list):
for blk in content:
if isinstance(blk, dict) and blk.get("type") in {
"tool_use", "tool_result",
}:
return True
return False
def _tool_output_contains(events, *needles):
"""True iff any tool_end.result / tool_result.content /
tool-role message content contains a needle. Inspects
the tool's own output, not the model's narration."""
for raw in events:
try:
ev = json.loads(raw)
except (json.JSONDecodeError, TypeError):
continue
if not isinstance(ev, dict):
continue
if ev.get("type") == "tool_end":
result = ev.get("result")
if isinstance(result, str) and any(n in result for n in needles if n):
return True
if ev.get("type") == "tool_result":
content = ev.get("content")
if isinstance(content, str) and any(n in content for n in needles if n):
return True
if isinstance(content, list):
for blk in content:
if isinstance(blk, dict):
text = blk.get("text") or blk.get("content")
if isinstance(text, str) and any(n in text for n in needles if n):
return True
for choice in ev.get("choices", []) or []:
delta = (choice or {}).get("delta") or {}
msg = (choice or {}).get("message") or {}
for src in (delta, msg):
if src.get("role") == "tool":
content = src.get("content") or ""
if isinstance(content, str) and any(n in content for n in needles if n):
return True
return False
# ── 1. Standard OpenAI function calling ──────────────────────
weather_tool = {
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a city.",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
},
}
status, data = post("/v1/chat/completions", {
"messages": [{"role": "user", "content": "What is the weather in Paris?"}],
"tools": [weather_tool],
"tool_choice": "required",
"stream": False,
"temperature": 0.0,
"seed": SEED,
"max_tokens": 120,
})
assert status == 200, f"tool call status {status}: {data}"
choice = data["choices"][0]
assert choice["finish_reason"] == "tool_calls", f"finish_reason={choice['finish_reason']!r}"
tc = choice["message"]["tool_calls"][0]
assert tc["function"]["name"] == "get_weather"
args = json.loads(tc["function"]["arguments"])
assert args.get("city"), f"missing city arg: {args}"
print(f"[tools] PASS function calling -> {tc['function']['name']}({args})")
# T=0 = deterministic argmax in llama.cpp; T>0 lets seed
# rotation explore distinct trajectories on retry.
TOOL_PROBE_TEMP = 0.4
def _run_tool_probe(*, label, prompt, enabled, session, needles,
max_attempts = 4):
"""Drive a server-side tool with retries. Hard FAIL if no
attempt has structural invocation evidence. WARN (not
FAIL) if invoked but no attempt produces the expected
literal in tool_end.result -- small-quant Qwen3.5-2B can
emit OpenAI tool_calls deltas without Studio's GGUF
agentic loop intercepting them, and that GGUF-vs-OpenAI
format mismatch is out of scope for #5642.
"""
attempts_log = []
best = None
for attempt_i in range(max_attempts):
attempt_seed = SEED + attempt_i
content, events = post_sse("/v1/chat/completions", {
"messages": [{"role": "user", "content": prompt}],
"enable_tools": True,
"enabled_tools": enabled,
"session_id": f"{session}-att{attempt_i}",
"temperature": TOOL_PROBE_TEMP,
"seed": attempt_seed,
"max_tokens": 600,
})
invoked = _tool_invoked(events)
produced = _tool_output_contains(events, *needles)
attempts_log.append({
"attempt": attempt_i, "seed": attempt_seed,
"n_events": len(events),
"tool_invoked": invoked, "tool_output_contains": produced,
"content_len": len(content),
})
if invoked and produced:
print(f"[tools] PASS {label} attempt {attempt_i}")
return content, events, attempts_log
if invoked and best is None:
best = (content, events)
print(f"[tools] retry {label} attempt {attempt_i}: invoked={invoked} output_ok={produced} events={len(events)}")
if best is not None:
print(f"[tools] WARN {label}: invoked but no tool_end.result match (small-quant flake). Attempts: {attempts_log}")
content, events = best
return content, events, attempts_log
raise AssertionError(
f"{label}: no structural tool-invocation evidence across "
f"{max_attempts} attempts. enable_tools may be silently "
f"ignored. Attempts: {attempts_log}"
)
# ── 2. Server-side python tool ───────────────────────────────
content, events, _attempts = _run_tool_probe(
label = "python tool",
prompt = "What is 123 * 456? Use the python tool to compute it and tell me the number.",
enabled = ["python"],
session = "ci-tool-calling-py",
needles = ("56088", "56,088"),
)
if "56088" in content or "56,088" in content:
print(f"[tools] python tool narration OK")
else:
print(f"[tools] python tool narration drifted -- content={content!r}")
# ── 3. Server-side bash (terminal) tool ──────────────────────
content, events, _attempts = _run_tool_probe(
label = "bash/terminal tool",
prompt = "Use the terminal tool to run `echo hello-bash-tool` and tell me the exact output.",
enabled = ["terminal"],
session = "ci-tool-calling-bash",
needles = ("hello-bash-tool",),
)
if "hello-bash-tool" in content:
print(f"[tools] bash/terminal narration OK")
else:
print(f"[tools] bash/terminal narration dropped literal -- content={content!r}")
# ── 4. Server-side web_search tool ───────────────────────────
# DuckDuckGo is flaky from CI runners and small Qwen3.5-2B
# may not actually search. Only assert that the SSE stream
# opens and yields any data; HTTP / parser failures already
# raise above. Tool-invocation strictness is relaxed here
# because (a) the search may legitimately return no results,
# and (b) DuckDuckGo upstream blocks GHA IP ranges often
# enough that requiring a tool_call marker would create
# red-herring failures from infra rather than from Studio.
try:
content, events = post_sse("/v1/chat/completions", {
"messages": [{"role": "user", "content": "Search the web for 'unsloth ai github' and summarise."}],
"enable_tools": True,
"enabled_tools": ["web_search"],
"session_id": "ci-tool-calling-web",
"temperature": 0.0,
"seed": SEED,
"max_tokens": 400,
})
print(
f"[tools] PASS web_search stream ({len(content)} chars in content, "
f"{len(events)} raw events)"
)
except Exception as exc:
print(f"[tools] WARN web_search probe failed (non-blocking): {exc}")
# ── 5. Thinking on / off ─────────────────────────────────────
# Studio strips think blocks from message.content for tools-mode
# responses, so we toggle plain chat (no enable_tools) and look
# at the surfaced reasoning_content / message.thinking field.
def thinking_call(enable):
status, data = post("/v1/chat/completions", {
"messages": [{"role": "user", "content": "Briefly: is 17 prime?"}],
"stream": False,
"enable_thinking": enable,
"temperature": 0.0,
"seed": SEED,
"max_tokens": 300,
})
assert status == 200
msg = data["choices"][0]["message"]
# Studio surfaces thinking via reasoning_content (OpenAI
# extension). Fall back to inline <think> markers for
# robustness across template versions.
raw = (msg.get("content") or "") + (msg.get("reasoning_content") or "")
return raw
on_text = thinking_call(True)
off_text = thinking_call(False)
had_think_on = ("<think>" in on_text) or len(on_text) > 80
had_think_off = ("<think>" in off_text) and len(off_text) > 0
assert had_think_on, (
f"enable_thinking=True produced no thinking signal: {on_text!r}"
)
# Off-mode should not contain the literal <think> marker.
assert "<think>" not in off_text, (
f"enable_thinking=False but <think> still present: {off_text!r}"
)
print(f"[tools] PASS thinking on/off (on={len(on_text)} chars, off={len(off_text)} chars)")
PY
- name: Stop Studio
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
ss -tln | grep ":${STUDIO_PORT}" || true
# Capture backend + llama-server logs so a 500 has a server-side traceback.
mkdir -p logs/server-logs
cp -r ~/.unsloth/studio/logs/. logs/server-logs/ 2>/dev/null || true
- name: Upload logs
# Always upload so green runs are still reviewable.
if: always()
# Diagnostic only: a transient artifact-service drop must not fail a green job.
continue-on-error: true
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: tool-calling-log
path: |
logs/studio.log
logs/install.log
logs/server-logs/
retention-days: 7
# ─────────────────────────────────────────────────────────────────────
# Job 3: JSON, images
# ─────────────────────────────────────────────────────────────────────
json-images:
name: JSON, images
runs-on: ubuntu-latest
timeout-minutes: 30
env:
GGUF_REPO: unsloth/Qwen3-VL-2B-Instruct-GGUF
# UD-Q4_K_XL, not UD-IQ2_XXS: at 2-bit the temp-0 answer to the JSON
# step's capital-of-France probe flips with the host's SIMD kernels
# (GitHub runners deterministically answered France while other CPUs
# answer Paris; seeds do not rescue it, 1/5 Paris at temp 0.7). The
# Q4 quant answered Paris 13/13 across temps and seeds on the same
# runners, so the hard Paris assertion below stays reliable.
GGUF_VARIANT: UD-Q4_K_XL
GGUF_FILE: Qwen3-VL-2B-Instruct-UD-Q4_K_XL.gguf
MMPROJ_FILE: mmproj-F16.gguf
STUDIO_PORT: '18890'
HF_HOME: ${{ github.workspace }}/hf-cache
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
with:
persist-credentials: false
- name: Linux deps for llama.cpp prebuilt
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
libcurl4-openssl-dev libssl-dev jq
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6.4.0
with:
node-version: '22'
- uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
with:
python-version: '3.12'
cache: 'pip'
- name: Restore HF_HOME for ${{ env.GGUF_REPO }} (model + mmproj)
id: cache-hf
uses: actions/cache/restore@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
continue-on-error: true
with:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-${{ env.MMPROJ_FILE }}-v2
- name: Prime HF_HOME with the GGUF + mmproj
id: prime-hf
if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
env:
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
run: |
python -m pip install --upgrade huggingface_hub
mkdir -p hf-cache
bash .github/scripts/hf-download-with-retry.sh "$GGUF_REPO" "$GGUF_FILE"
bash .github/scripts/hf-download-with-retry.sh "$GGUF_REPO" "$MMPROJ_FILE"
bash .github/scripts/hf-download-with-retry.sh ggml-org/models tinyllamas/stories260K.gguf
- name: Save HF_HOME for ${{ env.GGUF_REPO }} (model + mmproj)
if: always() && steps.prime-hf.outcome == 'success'
uses: actions/cache/save@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
with:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-${{ env.MMPROJ_FILE }}-v2
- name: Install Studio (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
run: |
mkdir -p logs
set -o pipefail
bash install.sh --local --no-torch 2>&1 | tee logs/install.log
- name: Install OpenAI + Anthropic Python SDKs
run: pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
# See Job 2's comment: API-only mode keeps tool_policy=None so
# response_format requests aren't routed through the agentic
# tool loop.
run: |
unsloth studio reset-password
mkdir -p logs
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p "$STUDIO_PORT" \
> logs/studio.log 2>&1 &
echo "STUDIO_PID=$!" >> "$GITHUB_ENV"
- name: Wait for /api/health, log in, change password, load model
run: |
for i in $(seq 1 180); do
if curl -fs "http://127.0.0.1:${STUDIO_PORT}/api/health" > /tmp/health.json; then
jq -e '.status == "healthy"' /tmp/health.json && break
fi
sleep 1
done
jq -e '.status == "healthy"' /tmp/health.json
OLD=$(cat ~/.unsloth/studio/auth/.bootstrap_password)
NEW="CIJson-$(python -c 'import secrets; print(secrets.token_urlsafe(12))')"
echo "::add-mask::$OLD"
echo "::add-mask::$NEW"
OLD_TOKEN=$(curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$OLD\"}" | jq -r .access_token)
curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/change-password" \
-H "Authorization: Bearer $OLD_TOKEN" -H 'content-type: application/json' \
-d "{\"current_password\":\"$OLD\",\"new_password\":\"$NEW\"}" > /dev/null
TOKEN=$(curl -fs -X POST "http://127.0.0.1:${STUDIO_PORT}/api/auth/login" \
-H 'content-type: application/json' \
-d "{\"username\":\"unsloth\",\"password\":\"$NEW\"}" | jq -r .access_token)
echo "API_KEY=$TOKEN" >> "$GITHUB_ENV"
# Retry: llama-server startup can race process teardown after a
# failed attempt. Keep curl out of a pipe so HTTP failures are not
# masked by jq.
LOAD_OK=0
for attempt in 1 2 3; do
HTTP=$(curl -s -o /tmp/load.json -w '%{http_code}' \
-X POST "http://127.0.0.1:${STUDIO_PORT}/api/inference/load" \
-H "Authorization: Bearer $TOKEN" -H 'content-type: application/json' \
--max-time 900 \
-d "{\"model_path\":\"$GGUF_REPO\",\"gguf_variant\":\"$GGUF_VARIANT\",\"is_lora\":false,\"max_seq_length\":2048}")
if [ "$HTTP" = "200" ]; then LOAD_OK=1; break; fi
echo "::warning::/api/inference/load attempt $attempt returned $HTTP; response:"
cat /tmp/load.json || true
sleep 10
done
[ "$LOAD_OK" = "1" ] || { echo "::error::/api/inference/load failed 3 attempts"; exit 22; }
jq '{status, display_name, is_vision}' /tmp/load.json
- name: JSON schema decoding + image input
env:
BASE_URL: http://127.0.0.1:18890
run: |
python - <<'PY'
import base64
import json
import os
import urllib.request
from openai import OpenAI
from anthropic import Anthropic
BASE = os.environ["BASE_URL"]
KEY = os.environ["API_KEY"]
SEED = 3407
def post(path, body, *, timeout = 240):
req = urllib.request.Request(
f"{BASE}{path}",
data = json.dumps(body).encode(),
method = "POST",
headers = {
"Authorization": f"Bearer {KEY}",
"Content-Type": "application/json",
},
)
with urllib.request.urlopen(req, timeout = timeout) as resp:
return resp.status, json.loads(resp.read().decode())
# ── 1. response_format = json_object (JSON mode) ─────────────
# llama.cpp's HTTP server supports OpenAI-compatible JSON
# mode: `response_format: {"type": "json_object"}` constrains
# the model to emit syntactically-valid JSON. We use raw HTTP
# rather than the OpenAI SDK so that the field shape Studio
# forwards to llama-server is unambiguous (the SDK rewrites
# response_format depending on which variant it recognises).
# We deliberately do NOT pass a strict JSON schema -- on
# small Gemma-4 quants the GBNF-from-schema path occasionally
# produces empty output, and JSON mode is the surface we care
# about exposing through Studio.
status, data = post("/v1/chat/completions", {
"model": "default",
"messages": [
{"role": "system", "content": 'Reply with a single JSON object of the form {"city": "...", "country": "..."}. Output ONLY the JSON, nothing else.'},
{"role": "user", "content": "What is the capital of France?"},
],
"temperature": 0.0,
"max_tokens": 200,
"seed": SEED,
"stream": False,
"enable_thinking": False,
"response_format": {"type": "json_object"},
}, timeout = 600)
assert status == 200, f"json status {status}: {data}"
content = (data["choices"][0]["message"].get("content") or "").strip()
# Some chat templates wrap JSON in ```json fences even in JSON
# mode -- strip those before parsing.
if content.startswith("```"):
content = content.split("```", 2)[1]
if content.startswith("json"):
content = content[4:]
content = content.strip("`\n ")
parsed = json.loads(content)
assert "paris" in str(parsed.get("city", "")).lower(), (
f"city != Paris: {parsed}"
)
print(f"[json] PASS json_object -> {parsed}")
# ── 2. OpenAI image_url (data URI base64) ───────────────────
# 64x64 solid-red PNG. stb_image (used by Studio's image
# normaliser at routes/inference.py:3410) rejects 4x4 or
# smaller PNGs as truncated, so we go up to 64x64 -- still
# tiny in token cost. The assertion is loose: any non-empty
# response from the vision path proves multimodal end-to-end
# wiring; small VL quants are weak at colour identification.
PNG_64X64_RED_B64 = (
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAIAAAAlC+aJAAAAYklEQVR4nO3PMQ0AIADAMEAI/k"
"UhBhEcDcmqYJtn7/GzpQNeNaA1oDWgNaA1oDWgNaA1oDWgNaA1oDWgNaA1oDWgNaA1oDWgNaA"
"1oDWgNaA1oDWgNaA1oDWgNaA1oDWgNaA1oDWgNaBdCJ0BmMJ25zMAAAAASUVORK5CYII="
)
data_uri = f"data:image/png;base64,{PNG_64X64_RED_B64}"
client = OpenAI(base_url = f"{BASE}/v1", api_key = KEY)
openai_resp = client.chat.completions.create(
model = "default",
temperature = 0.0,
max_tokens = 80,
seed = SEED,
messages = [{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": data_uri}},
{"type": "text", "text": "What colour dominates this image? Reply in one word."},
],
}],
)
openai_text = (openai_resp.choices[0].message.content or "").lower()
print(f"[image/openai] reply: {openai_text!r}")
assert openai_text, "OpenAI image_url returned empty content"
# We do not strictly require 'red' -- some quants of small VL
# models are weak at colour names. Just require a non-empty
# answer; the vision path is the part under test.
print("[image/openai] PASS image_url accepted, non-empty response")
# ── 3. Anthropic source/base64 image ────────────────────────
# Two SDK quirks vs. Studio: base_url must NOT include /v1
# (the SDK appends it itself; otherwise /v1/v1/messages -> 405),
# and Studio's auth is HTTPBearer-only so the SDK's default
# x-api-key header is ignored -- send Authorization: Bearer
# via default_headers.
anthropic = Anthropic(
base_url = BASE,
api_key = "unused",
default_headers = {"Authorization": f"Bearer {KEY}"},
)
a_msg = anthropic.messages.create(
model = "default",
max_tokens = 80,
temperature = 0.0,
extra_body = {"seed": SEED},
messages = [{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": PNG_64X64_RED_B64,
},
},
{"type": "text", "text": "Describe this image briefly."},
],
}],
)
a_text = "".join(b.text for b in a_msg.content if getattr(b, "type", None) == "text")
print(f"[image/anthropic] reply: {a_text!r}")
assert a_text, "Anthropic source/base64 returned empty content"
print("[image/anthropic] PASS source/base64 accepted, non-empty response")
PY
- name: Stop Studio
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
ss -tln | grep ":${STUDIO_PORT}" || true
- name: Upload logs
# Always upload so green runs are still reviewable.
if: always()
# Diagnostic only: a transient artifact-service drop must not fail a green job.
continue-on-error: true
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: json-images-log
path: |
logs/studio.log
logs/install.log
retention-days: 7