Case: 20260426T062915Z__gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked
TIMEOUT Trajectory: clean | Invoked: yes | Match: 0%
gptme --non-interactive --no-confirm --name gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-warmup --system short --workspace /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/workspace-warmup --tools shell --tool-format tool -m local/gemma4:e4b 'Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL. '
gptme --non-interactive --no-confirm --name gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-measured --system short --workspace /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/workspace --tools shell --tool-format tool -m local/gemma4:e4b 'Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL. '
Consistency: consistent
Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL.
{"query": "weekly plan a5c32b39d55b checkbox", "selected_id": "doc-7faa9e2b", "selected_score": 0.97, "selected_url": "https://kb.example/2910cb57f2e9"}
REQUEST_HIT=/search/x2ajH4UvGNjhi93Gm-q_YkvZ
MISSING_FILE
--- expected
+++ observed
@@ -1,2 +1 @@
-{"query": "weekly plan a5c32b39d55b checkbox", "selected_id": "doc-7faa9e2b", "selected_score": 0.97, "selected_url": "https://kb.example/2910cb57f2e9"}
-REQUEST_HIT=/search/x2ajH4UvGNjhi93Gm-q_YkvZ
+MISSING_FILE
No structured session transcript found.
[gripprobe] process_started_at=2026-04-26T06:37:02+00:00
[06:37:04] Created config file at /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/home/.config/gptme/config.toml
[06:37:04] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/gemma4:e4b
Using logdir: /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/state/gptme-logs/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-warmup
Using workspace: /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/workspace-warmup
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-url.txt, query.txt, and required-token.txt from the current workspace.
Call the search endpoint with query parameters:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
The endpoint returns JSON with a results array.
Select the result with the highest score among results whose snippet contains the exact required token.
Write search-result.json in the current workspace as JSON with exactly these keys:
- query
- selected_id
- selected_url
- selected_score
If successful, reply only DONE.
If command execution fails, reply only FAIL.
````search-url.txt
http://127.0.0.1:38199/search/8_l22Uli_0FMzeT_HkwVMiic
````
````query.txt
warmup query 850aabcb
````
````required-token.txt
69acc9b95d8f9571
````
Assistant: Thinking...
Assistant:
<think>
<think>
The user wants me to perform a multi-step task using the shell tool.
The goal is to:
1. Read three files: `search-url.txt`, `query.txt`, and `required-token.txt`.
2. Call a search endpoint using the content of these files as parameters.
3. The search endpoint URL is in `search-url.txt`.
4. The query parameter `q` is the URL-encoded content of `query.txt`.
5. The search parameters are: `q=<urlencoded query>`, `format=json`, `limit=8`.
6. The endpoint returns JSON with a `results` array.
7. I need to select the result from the `results` array that has the highest score AND whose snippet contains the exact required token (from `required-token.txt`).
8. Finally, I must write a JSON file named `search-result.json` with specific keys: `query`, `selected_id`, `selected_url`, `selected_score`.
9. If successful, reply only "DONE". If it fails, reply only "FAIL".
Since I need to process files, make an HTTP request, parse JSON, and perform logic (finding max score, checking token), I will likely need a combination of shell commands, possibly involving `curl` and `jq` (or similar JSON processing tools).
Step 1: Read the files.
Step 2: URL-encode the query from `query.txt`.
Step 3: Construct the full search URL.
Step 4: Execute the search request using `curl`.
Step 5: Parse the JSON response using `jq` to find the best result.
Step 6: Write the final JSON structure to `search-result.json`.
Let's start by listing the files to confirm they are present and then proceed with the logic. Since the user provided the content, I assume they are available in the current working directory.
I will use `curl` for the HTTP request and `jq` for JSON manipulation.
First, I need to read the content of the files.
File contents:
`search-url.txt`: `http://127.0.0.1:38199/search/8_l22Uli_0FMzeT_HkwVMiic`
`query.txt`: `warmup query 850aabcb`
`required-token.txt`: `69acc9b95d8f9571`
The query needs URL encoding. I'll use `jq` or a combination of tools if available, but standard shell tools might require a dedicated encoding step. Assuming `jq` is available, it might handle some encoding, but for robust URL encoding, I might need a dedicated utility or rely on `curl`'s ability to handle parameters if I construct the URL correctly.
Let's assume the query encoding can be done via shell tools or that the search endpoint expects the raw query string and I need to encode it manually or use a helper function/tool if available. Since I don't have a dedicated URL encoding tool, I'll use `python` or `perl` if available, or assume a simple shell approach for now, but I must be careful about encoding.
Let's try to use `python` for encoding, as it's generally available in environments where `curl` and `jq` are.
**Plan refinement:**
1. **Encode Query:** Read `query.txt` and URL-encode its content.
2. **Construct URL:** Combine the base URL from `search-url.txt` with the encoded query and parameters (`?q=<encoded_query>&format=json&limit=8`).
3. **Fetch Data:** Use `curl` to hit the constructed URL.
4. **Process Data:** Use `jq` to iterate through results, filter by token presence in snippet, and find the maximum score.
5. **Output:** Extract required fields and write to `search-result.json`.
Let's start with the encoding step.
</think>
</think>
<thinking>
<thinking>
The user wants me to execute a complex task involving file reading, URL encoding, making an HTTP request, JSON parsing, and data selection/filtering.
1. **Read Files:** I need the content of `search-url.txt`, `query.txt`, and `required-token.txt`.
2. **URL Encoding:** The query from `query.txt` must be URL-encoded. I will use Python for reliable URL encoding.
3. **Construct URL:** The final URL will be constructed using the base URL, the encoded query, and fixed parameters (`format=json`, `limit=8`).
4. **Fetch Data:** Use `curl` to call the endpoint.
5. **Process Data:** Use `jq` to process the JSON response. The logic is:
* Iterate through `results`.
* Filter: `snippet` must contain the exact token from `required-token.txt`.
* Select: The result with the maximum `score` among the filtered results.
6. **Output:** Write the required fields (`query`, `selected_id`, `selected_url`, `selected_score`) to `search-result.json`.
7. **Final Reply:** "DONE" or "FAIL".
**Step 1 & 2: Encoding the Query**
I will read `query.txt` and encode it.
**Step 3 & 4: Constructing and Calling the URL**
I will construct the full URL and use `curl`.
**Step 5 & 6: Processing and Outputting**
I will use `jq` to implement the selection logic.
Let's start by encoding the query.
</thinking>
</thinking>
@shell(call_uxm6s85l): {"command":"python3 -c \"import urllib.parse; print(urllib.parse.quote('warmup query 850aabcb'))\""}
Preview
python3 -c "import urllib.parse; print(urllib.parse.quote('warmup query 850aabcb'))"
warmup%20query%20850aabcb
System:
Ran command: `python3 -c "import urllib.parse; print(urllib.parse.quote('warmup query 850aabcb'))"`
`warmup%20query%20850aabcb`
[06:38:30] ERROR exception during auto-name auto_naming.py:100
Traceback (most recent call last):
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/util/auto_naming.py", line 97, in _generate_llm_name
summary_model_name = get_summary_model(current_model.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/llm/models.py", line 576, in get_summary_model
raise ValueError(f"Provider {provider} did not have a summary model")
ValueError: Provider local did not have a summary model
Assistant: Thinking...
[06:38:49] INFO Reasoning content: Thinking Process: llm_openai.py:321
1. **Analyze the Request:** The user is providing a transcript snippet (a user instruction followed by an assistant's internal thought process) and asking for a concise, 2-4 word title.
2. **Analyze the Content:**
* User instruction: "Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters..."
* Assistant thought: Confirms the goal is a multi-step task using the shell tool to read files and call a search endpoint.
3. **Identify the Core Topic:** The conversation is about using a shell tool to automate a search query using multiple input files and an API endpoint.
4. **Draft Potential Titles (Focusing on conciseness and accuracy):**
* Shell tool search query
* Automated search endpoint call
* Using shell for search
* Search API using shell
5. **Select the Best Fit (2-4 words, capturing the action and tool):** "Search API using shell" or "Shell tool search query" are both good. "Search API using shell" is slightly more descriptive of the *purpose*.
6. **Final Selection:** Search API using shell. (Meets all constraints: 4 words, captures the main topic, concise).
INFO Auto-generated conversation name: Search API using shell chat.py:332
Assistant:
@shell(call_7wpx1h38): {"command":"python3 -c \"import urllib.parse; print(urllib.parse.quote('warmup query 850aabcb'))\""}
Preview
python3 -c "import urllib.parse; print(urllib.parse.quote('warmup query 850aabcb'))"
warmup%20query%20850aabcb
System:
Ran command: `python3 -c "import urllib.parse; print(urllib.parse.quote('warmup query 850aabcb'))"`
`warmup%20query%20850aabcb`
Assistant: Thinking...
Assistant:
<thinking>
<thinking>
The query encoding is complete: `warmup%20query%20850aabcb`.
Now I have all the necessary components:
* Base URL: `http://127.0.0.1:38199/search/8_l22Uli_0FMzeT_HkwVMiic` (from `search-url.txt`)
* Encoded Query: `warmup%20query%20850aabcb`
[gripprobe] process_finished_at=2026-04-26T06:39:02+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T06:37:02+00:00 [gripprobe] process_finished_at=2026-04-26T06:39:02+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T06:39:02+00:00
[06:39:04] Created config file at /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/home/.config/gptme/config.toml
[06:39:04] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/gemma4:e4b
Using logdir: /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/state/gptme-logs/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked-measured
Using workspace: /work/results/runs/20260426T062915Z/cases/gptme__local_gemma4_e4b__ollama__tool__web_search_json_ranked/workspace
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-url.txt, query.txt, and required-token.txt from the current workspace.
Call the search endpoint with query parameters:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
The endpoint returns JSON with a results array.
Select the result with the highest score among results whose snippet contains the exact required token.
Write search-result.json in the current workspace as JSON with exactly these keys:
- query
- selected_id
- selected_url
- selected_score
If successful, reply only DONE.
If command execution fails, reply only FAIL.
````search-url.txt
http://127.0.0.1:38199/search/x2ajH4UvGNjhi93Gm-q_YkvZ
````
````query.txt
weekly plan a5c32b39d55b checkbox
````
````required-token.txt
69acc9b95d8f9571
````
Assistant: Thinking...
Assistant:
<think>
<think>
The user wants me to perform a multi-step task using the `shell` tool.
The steps are:
1. Read three files: `search-url.txt`, `query.txt`, and `required-token.txt`.
2. Construct a search query using the content of these files.
3. Call a search endpoint (provided in `search-url.txt`) with specific query parameters:
* `q=<urlencoded query from query.txt>`
* `format=json`
* `limit=8`
4. The endpoint returns JSON with a `results` array.
5. Process the results: Select the result with the highest score among those whose snippet contains the exact required token (from `required-token.txt`).
6. Write the selected result's metadata (`query`, `selected_id`, `selected_url`, `selected_score`) into a new file named `search-result.json` in the current workspace.
7. If successful, reply only `DONE`.
8. If command execution fails, reply only `FAIL`.
Since I need to read files, construct a URL, make an HTTP request, and then parse/process JSON data, I will likely need tools like `curl` or a scripting language (like Python/JavaScript) within the shell environment. Given the complexity of URL encoding, JSON parsing, and conditional logic, using a scripting language like Python via the shell tool is the most robust approach.
Let's start by reading the files and then planning the execution flow.
**File contents:**
* `search-url.txt`: `http://127.0.0.1:38199/search/x2ajH4UvGNjhi93Gm-q_YkvZ` (This is the base URL)
* `query.txt`: `weekly plan a5c32b39d55b checkbox` (This needs URL encoding)
* `required-token.txt`: `69acc9b95d8f9571` (This is the exact token to match in the snippet)
**Execution Plan:**
1. Read the file contents into variables/scripts.
2. URL encode the content of `query.txt`.
3. Construct the full search URL.
4. Execute the search request using `curl`.
5. Use Python/Jinja/jq (if available, Python is safer) to parse the JSON response, filter results, find the max score, and write the output JSON.
I will use Python within the shell tool for maximum control over encoding and JSON manipulation.
**Step 1: Read files and prepare variables.**
I will assume the files are present in the current working directory.
**Step 2: URL Encoding.**
I need to URL encode the content of `query.txt`.
**Step 3: Construct URL and execute search.**
The endpoint seems to be the base URL, and the parameters are appended.
Let's write a single Python script execution block to handle all logic.
</think>
</think>
<thinking>
<thinking>
The user wants me to execute a complex task involving file reading, URL encoding, making an HTTP request, JSON parsing, filtering, and writing a final JSON output.
I will use a Python script executed via the `shell` tool to handle all these steps robustly.
**Plan:**
1. Read the content of `search-url.txt`, `query.txt`, and `required-token.txt`.
2. Use Python's `urllib.parse` to URL-encode the query from `query.txt`.
3. Construct the full search URL using the base URL and the required parameters (`q`, `format=json`, `limit=8`).
4. Use `requests` (or `urllib.request` if `requests` is not guaranteed) to fetch the data from the constructed URL. Since I am in a shell environment, `curl` combined with Python for parsing is often safer, but let's try to keep it all in Python for consistency. I will use `requests` as it's standard for this kind of task, assuming it's available in the environment.
5. Parse the JSON response.
6. Iterate through the `results` array, filtering for snippets containing the `required_token`.
7. Keep track of the result with the highest score among the filtered results.
8. Write the final structured JSON to `search-result.json`.
9. Finally, check the success/failure condition and output `DONE` or `FAIL`.
Let's start by writing the Python script logic.
</thinking>
</thinking>
@shell(call_cpr6vdh1): {"command":"python3 -c \"\nimport json\nimport urllib.parse\nimport requests\nimport os\n\n# 1. Read file contents\ntry:\n search_url_file = 'search-url.txt'\n query_file = 'query.txt'\n token_file = 'required-token.txt'\n\n with open(search_url_file, 'r') as f:\n base_url = f.read().strip()\n \n with open(query_file, 'r') as f:\n raw_query = f.read().strip()\n \n with open(token_file, 'r') as f:\n required_token = f.read().strip()\n\nexcept FileNotFoundError as e:\n print(f'FAIL: Missing file {e.filename}')\n exit(1)\n\n# 2. URL Encode the query\nencoded_query = urllib.parse.quote(raw_query)\n\n# 3. Construct the full search URL\n# Parameters: q=\u003cencoded query\u003e, format=json, limit=8\nparams = {\n 'q': encoded_query,\n 'format': 'json',\n 'limit': '8'\n}\n\n# The base_url already contains the path, so we use params for query string construction\nsearch_url = f'{base_url}?{urllib.parse.urlencode(params)}'\n\nprint(f'Searching URL: {search_url}')\n\n# 4. Execute the search request\ntry:\n # Using requests library for HTTP call\n response = requests.get(search_url, timeout=10)\n response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)\n data = response.json()\nexcept requests.exceptions.RequestException as e:\n print(f'FAIL: HTTP Request failed: {e}')\n exit(1)\nexcept json.JSONDecodeError:\n print('FAIL: Failed to decode JSON response.')\n exit(1)\n\n# 5. Process results\nresults = data.get('results', [])\nselected_result = None\nmax_score = -1\n\nfor result in results:\n snippet = result.get('snippet', '')\n score = result.get('score', 0)\n \n # Check if the snippet contains the exact required token\n if required_token in snippet:\n if score \u003e max_score:\n max_score = score\n selected_result = result\n\n# 6. Write the final JSON\nif selected_result:\n output_data = {\n 'query': raw_query,\n 'selected_id': selected_result.get('id'),\n 'selected_url': selected_result.get('url'),\n 'selected_score': selected_result.get('score')\n }\n \n try:\n with open('search-result.json', 'w') as f:\n json.dump(output_data, f, indent=4)\n print('SUCCESS')\n except Exception as e:\n print(f'FAIL: Could not write search-result.json: {e}')\n exit(1)\nelse:\n print('FAIL: No result found containing the required token.')\n exit(1)\n\""}
Preview
python3 -c "
import json
import urllib.parse
import requests
import os
# 1. Read file contents
try:
search_url_file = 'search-url.txt'
query_file = 'query.txt'
token_file = 'required-token.txt'
with open(search_url_file, 'r') as f:
base_url = f.read().strip()
with open(query_file, 'r') as f:
raw_query = f.read().strip()
with open(token_file, 'r') as f:
required_token = f.read().strip()
except FileNotFoundError as e:
print(f'FAIL: Missing file {e.filename}')
exit(1)
# 2. URL Encode the query
encoded_query = urllib.parse.quote(raw_query)
# 3. Construct the full search URL
# Parameters: q=<encoded query>, format=json, limit=8
params = {
'q': encoded_query,
'format': 'json',
'limit': '8'
}
# The base_url already contains the path, so we use params for query string construction
search_url = f'{base_url}?{urllib.parse.urlencode(params)}'
print(f'Searching URL: {search_url}')
# 4. Execute the search request
try:
# Using requests library for HTTP call
response = requests.get(search_url, timeout=10)
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
data = response.json()
except requests.exceptions.RequestException as e:
print(f'FAIL: HTTP Request failed: {e}')
exit(1)
except json.JSONDecodeError:
print('FAIL: Failed to decode JSON response.')
exit(1)
# 5. Process results
results = data.get('results', [])
selected_result = None
max_score = -1
for result in results:
snippet = result.get('snippet', '')
score = result.get('score', 0)
# Check if the snippet contains the exact required token
if required_token in snippet:
if score > max_score:
max_score = score
selected_result = result
# 6. Write the final JSON
if selected_result:
output_data = {
'query': raw_query,
'selected_id': selected_result.get('id'),
'selected_url': selected_result.get('url'),
'selected_score': selected_result.get('score')
}
try:
with open('search-result.json', 'w') as f:
json.dump(output_data, f, indent=4)
print('SUCCESS')
except Exception as e:
print(f'FAIL: Could not write search-result.json: {e}')
exit(1)
else:
print('FAIL: No result found containing the required token.')
exit(1)
"
System:
Ran command:
```bash
python3 -c "
import json
import urllib.parse
import requests
import os
# 1. Read file contents
try:
search_url_file = 'search-url.txt'
query_file = 'query.txt'
token_file = 'required-token.txt'
with open(search_url_file, 'r') as f:
base_url = f.read().strip()
with open(query_file, 'r') as f:
raw_query = f.read().strip()
with open(token_file, 'r') as f:
required_token = f.read().strip()
except FileNotFoundError as e:
print(f'FAIL: Missing file {e.filename}')
exit(1)
# 2. URL Encode the query
encoded_query = urllib.parse.quote(raw_query)
# 3. Construct the full search URL
# Parameters: q=<encoded query>, format=json, limit=8
params = {
'q': encoded_query,
'format': 'json',
'limit': '8'
}
# The base_url already contains the path, so we use params for query string construction
search_url = f'{base_url}?{urllib.parse.urlencode(params)}'
print(f'Searching URL: {search_url}')
# 4. Execute the search request
try:
# Using requests library for HTTP call
response = requests.get(search_url, timeout=10)
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
data = response.json()
except requests.exceptions.RequestException as e:
print(f'FAIL: HTTP Request failed: {e}')
exit(1)
except json.JSONDecodeError:
print('FAIL: Failed to decode JSON response.')
exit(1)
# 5. Process results
results = data.get('results', [])
selected_result = None
max_score = -1
for result in results:
snippet = result.get('snippet', '')
score = result.get('score', 0)
# Check if the snippet contains the exact required token
if required_token in snippet:
if score > max_score:
max_score = score
selected_result = result
# 6. Write the final JSON
if selected_result:
output_data = {
'query': raw_query,
'selected_id': selected_result.get('id'),
'selected_url': selected_result.get('url'),
'selected_score': selected_result.get('score')
}
try:
with open('search-result.json', 'w') as f:
json.dump(output_data, f, indent=4)
print('SUCCESS')
except Exception as e:
print(f'FAIL: Could not write search-result.json: {e}')
exit(1)
else:
print('FAIL: No result found containing the required token.')
exit(1)
"
```
```stderr
Traceback (most recent call last):
File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'requests'
```
Return code: 1
[06:41:00] ERROR exception during auto-name auto_naming.py:100
Traceback (most recent call last):
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/util/auto_naming.py", line 97, in _generate_llm_name
summary_model_name = get_summary_model(current_model.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/llm/models.py", line 576, in get_summary_model
raise ValueError(f"Provider {provider} did not have a summary model")
ValueError: Provider local did not have a summary model
Assistant: Thinking...
[gripprobe] process_finished_at=2026-04-26T06:41:02+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T06:39:02+00:00 Traceback (most recent call last): File "<string>", line 4, in <module> ModuleNotFoundError: No module named 'requests' [gripprobe] process_finished_at=2026-04-26T06:41:02+00:00 exit_code=124 timeout=true
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM gemma4:e4b
FROM /usr/share/ollama/.ollama/models/blobs/sha256-4c27e0f5b5adf02ac956c7322bd2ee7636fe3f45a8512c9aba5385242cb6e09a
TEMPLATE {{ .Prompt }}
RENDERER gemma4
PARSER gemma4
PARAMETER temperature 1
PARAMETER top_k 64
PARAMETER top_p 0.95
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