herzogfloria/LLM_Inferenz_Server_1
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Add LLM parameter controls to sidebar

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Thinking mode toggle, temperature, max tokens, top_p, and presence
penalty sliders in the Streamlit sidebar. Parameters apply to both
chat and file editor generation.

Made-with: Cursor
This commit is contained in:
herzogflorian 2026-03-02 16:41:05 +01:00
parent 9e1e0c0751
commit 12f9e3ac9b
1 changed files with 27 additions and 6 deletions
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33
app.py
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@ -18,6 +18,7 @@ from pathlib import Path
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
st.sidebar.header("Connection")
API_BASE = st.sidebar.text_input("API Base URL", "http://silicon.fhgr.ch:7080/v1")
API_KEY = st.sidebar.text_input("API Key", "EMPTY", type="password")
MODEL = "qwen3.5-35b-a3b"
@ -26,6 +27,23 @@ WORKSPACE.mkdir(exist_ok=True)
client = OpenAI(base_url=API_BASE, api_key=API_KEY)
# ---------------------------------------------------------------------------
# Sidebar — LLM Parameters
# ---------------------------------------------------------------------------
st.sidebar.markdown("---")
st.sidebar.header("LLM Parameters")
thinking_mode = st.sidebar.toggle("Thinking Mode", value=False,
help="Enable chain-of-thought reasoning. Better for complex tasks, slower for simple ones.")
temperature = st.sidebar.slider("Temperature", 0.0, 2.0, 0.7, 0.05,
help="Lower = deterministic, higher = creative.")
max_tokens = st.sidebar.slider("Max Tokens", 256, 16384, 4096, 256,
help="Maximum length of the response.")
top_p = st.sidebar.slider("Top P", 0.0, 1.0, 0.95, 0.05,
help="Nucleus sampling: only consider tokens within this cumulative probability.")
presence_penalty = st.sidebar.slider("Presence Penalty", 0.0, 2.0, 0.0, 0.1,
help="Penalize repeated topics. Higher values encourage the model to talk about new topics.")
LANG_MAP = {
".py": "python", ".tex": "latex", ".js": "javascript",
".html": "html", ".css": "css", ".sh": "bash",
@ -89,10 +107,12 @@ with tab_chat:
stream = client.chat.completions.create(
model=MODEL,
messages=st.session_state.messages,
max_tokens=8092,
temperature=0.2,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
presence_penalty=presence_penalty,
stream=True,
extra_body={"chat_template_kwargs": {"enable_thinking": True}},
extra_body={"chat_template_kwargs": {"enable_thinking": thinking_mode}},
)
for chunk in stream:
delta = chunk.choices[0].delta.content or ""
@ -168,9 +188,10 @@ with tab_editor:
f"Instruction: {gen_prompt}"
)},
],
max_tokens=16384,
temperature=0.6,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
extra_body={"chat_template_kwargs": {"enable_thinking": thinking_mode}},
)
result = response.choices[0].message.content
code = extract_code(result, lang)