Magistral-Small-2506-GGUF

./llama.cpp/llama-cli -hf unsloth/Magistral-Small-2506-GGUF:UD-Q4_K_XL --jinja --temp 0.7 --top-k -1 --top-p 0.95 -ngl 99

ollama run hf.co/unsloth/Magistral-Small-2506-GGUF:UD-Q4_K_XL

<s>[SYSTEM_PROMPT]system_prompt

A user will ask you to solve a task. You should first draft your thinking process (inner monologue) until you have derived the final answer. Afterwards, write a self-contained summary of your thoughts (i.e. your summary should be succinct but contain all the critical steps you needed to reach the conclusion). You should use Markdown to format your response. Write both your thoughts and summary in the same language as the task posed by the user. NEVER use \boxed{} in your response.

Your thinking process must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct answer.
</think>

Here, provide a concise summary that reflects your reasoning and presents a clear final answer to the user. Don't mention that this is a summary.

Problem:

[/SYSTEM_PROMPT][INST]user_message[/INST]<think>
reasoning_traces
</think>
assistant_response</s>[INST]user_message[/INST]

pip install -U vllm \
    --pre \
    --extra-index-url https://wheels.vllm.ai/nightly

python -c "import mistral_common; print(mistral_common.__version__)"

vllm serve mistralai/Magistral-Small-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.7
TOP_P = 0.95
MAX_TOK = 40_960

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt

SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")

query = "Write 4 sentences, each with at least 8 words. Now make absolutely sure that every sentence has exactly one word less than the previous sentence."
# or try out other queries
# query = "Exactly how many days ago did the French Revolution start? Today is June 4th, 2025."
# query = "Think about 5 random numbers. Verify if you can combine them with addition, multiplication, subtraction or division to 133"
# query = "If it takes 30 minutes to dry 12 T-shirts in the sun, how long does it take to dry 33 T-shirts?"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": query}
]
stream = client.chat.completions.create(
  model=model,
  messages=messages,
  stream=True,
  temperature=TEMP,
  top_p=TOP_P,
  max_tokens=MAX_TOK,
)

print("client: Start streaming chat completions...")
printed_content = False

for chunk in stream:
  content = None
  # Check the content is content
  if hasattr(chunk.choices[0].delta, "content"):
    content = chunk.choices[0].delta.content

  if content is not None:
    if not printed_content:
        printed_content = True
        print("\ncontent:", end="", flush=True)
    # Extract and print the content
    print(content, end="", flush=True)

# content:<think>
# Alright, I need to write 4 sentences where each one has at least 8 words and each subsequent sentence has one fewer word than the previous one.
# ...
# Final boxed answer (the four sentences):

# \[
# \boxed{
# \begin{aligned}
# &\text{1. The quick brown fox jumps over lazy dog and yells hello.} \\
# &\text{2. I saw the cat on the stair with my hat.} \\
# &\text{3. The man in the moon came down quickly today.} \\
# &\text{4. A cat sat on the mat today patiently.}
# \end{aligned}
# }
# \]