llama_get_logits_ith: invalid logits id -1, reason: no logits

# Prerequisites

Please answer the following questions for yourself before submitting an issue.

- [x] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [x] I carefully followed the [README.md](https://github.com/abetlen/llama-cpp-python/blob/main/README.md).
- [x] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
- [x] I reviewed the [Discussions](https://github.com/abetlen/llama-cpp-python/discussions), and have a new bug or useful enhancement to share.

# Expected Behavior
Get a response from model
# Current Behavior
llama_get_logits_ith: invalid logits id -1, reason: no logits
[1] 58786 segmentation fault python ragPhiGguf.py --model_path Phi-3.5-mini-instruct-Q4_K_M.gguf --query
/opt/homebrew/Cellar/python@3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/multiprocessing/resource_tracker.py:276: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown: {'/loky-58786-dm8v3d6t'}
warnings.warn(

# Environment and Context

* Physical (or virtual) hardware you are using, e.g. for Linux:
arm64 m2 pro

* Operating System, e.g. for Linux:
mac

* SDK version, e.g. for Linux:
Python 3.13.0

GNU Make 3.81
Copyright (C) 2006 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.
There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE.

This program built for i386-apple-darwin11.3.0

g++ --version
Apple clang version 16.0.0 (clang-1600.0.26.4)
Target: arm64-apple-darwin24.1.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin

# Failure Information (for bugs)

segmentation fault

# Steps to Reproduce

i was trying to build a RAG script using `Phi-3.5-mini-instruct-Q4_K_M.gguf` and i am using default chat template

```
Available chat formats from metadata: chat_template.default
Using gguf chat template: {% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|>
' + message['content'] + '<|end|>
'}}{% elif message['role'] == 'user' %}{{'<|user|>
' + message['content'] + '<|end|>
'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>
' + message['content'] + '<|end|>
'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
' }}{% else %}{{ eos_token }}{% endif %}
```

```
def generate_response(
self, query: str, context_docs: List[Dict], max_tokens: int = 1000
) -> str:
"""
Generate response using retrieved documents as context, formatted in GGUF chat template.
Args:
query (str): User query
context_docs (List[Dict]): Retrieved context documents
max_tokens (int): Maximum tokens to generate
Returns:
Generated response
"""
# Construct context from documents
context_texts = []
for doc in context_docs:
context_texts.append(
f"Article ID: {doc.get('article_id', 'N/A')
}\n{doc.get('text', '')}"
)
context = "\n\n".join(context_texts)
# Ensure context fits within the context window
context = self.truncate_text(context)

# Construct GGUF chat template prompt
prompt = (
"{% for message in messages %}"
"{% if message['role'] == 'system' and message['content'] %}"
"{{'<|system|>\n' + message['content'] + '<|end|>\n'}}"
"{% elif message['role'] == 'user' %}"
"{{'<|user|>\n' + message['content'] + '<|end|>\n'}}"
"{% elif message['role'] == 'assistant' %}"
"{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}"
"{% endif %}{% endfor %}"
"{% if add_generation_prompt %}"
"{{ '<|assistant|>\n' }}"
"{% else %}{{ eos_token }}{% endif %}"
)

template = Template(prompt)

# Replace placeholders with the actual chat history
messages = [
{"role": "system", "content": f"Context:\n{context}"},
{"role": "user", "content": query},
]
formatted_prompt = template.render(
messages=messages, add_generation_prompt=True, eos_token="<|endoftext|>"
)

# Generate response
try:
response = self.llm(
formatted_prompt,
max_tokens=max_tokens,
stop=["<|end|>"],
echo=False,
)
return response["choices"][0]["text"].strip()
except Exception as e:
self.logger.error(f"Response generation error: {e}")
return f"Error generating response: {str(e)}"
```