The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
This model is the reasoning post-trained version, trained for reasoning tasks, making it ideal for math, coding and stem related use cases.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.
Here we asumme that the model mistralai/Ministral-3-3B-Reasoning-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.
Vision Reasoning
Let's see if the Ministral 3 model knows when to pick a fight !
from typing import Any
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 = 262144
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) -> dict[str, Any]:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
index_begin_think = system_prompt.find("[THINK]")
index_end_think = system_prompt.find("[/THINK]")
return {
"role": "system",
"content": [
{"type": "text", "text": system_prompt[:index_begin_think]},
{
"type": "thinking",
"thinking": system_prompt[
index_begin_think + len("[THINK]") : index_end_think
],
"closed": True,
},
{
"type": "text",
"text": system_prompt[index_end_think + len("[/THINK]") :],
},
],
}
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
SYSTEM_PROMPT,
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
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...:\n")
printed_reasoning_content = False
answer = []
for chunk in stream:
reasoning_content = None
content = None
# Check the content is reasoning_content or content
if hasattr(chunk.choices[0].delta, "reasoning_content"):
reasoning_content = chunk.choices[0].delta.reasoning_content
if hasattr(chunk.choices[0].delta, "content"):
content = chunk.choices[0].delta.content
if reasoning_content is not None:
if not printed_reasoning_content:
printed_reasoning_content = True
print("Start reasoning:\n", end="", flush=True)
print(reasoning_content, end="", flush=True)
elif content is not None:
# Extract and print the content
if not reasoning_content and printed_reasoning_content:
answer.extend(content)
print(content, end="", flush=True)
if answer:
print("\n\n=============\nAnswer\n=============\n")
print("".join(answer))
else:
print("\n\n=============\nNo Answer\n=============\n")
print(
"No answer was generated by the model, probably because the maximum number of tokens was reached."
)
Now we'll make it compute some maths !
from typing import Any
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 = 262144
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) -> dict[str, Any]:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
index_begin_think = system_prompt.find("[THINK]")
index_end_think = system_prompt.find("[/THINK]")
return {
"role": "system",
"content": [
{"type": "text", "text": system_prompt[:index_begin_think]},
{
"type": "thinking",
"thinking": system_prompt[
index_begin_think + len("[THINK]") : index_end_think
],
"closed": True,
},
{
"type": "text",
"text": system_prompt[index_end_think + len("[/THINK]") :],
},
],
}
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
image_url = "https://i.ytimg.com/vi/5Y3xLHeyKZU/hqdefault.jpg"
messages = [
SYSTEM_PROMPT,
{
"role": "user",
"content": [
{
"type": "text",
"text": "Solve the equations. If they contain only numbers, use your calculator, else only think. Answer in the language of the image.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
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...:\n")
printed_reasoning_content = False
answer = []
for chunk in stream:
reasoning_content = None
content = None
# Check the content is reasoning_content or content
if hasattr(chunk.choices[0].delta, "reasoning_content"):
reasoning_content = chunk.choices[0].delta.reasoning_content
if hasattr(chunk.choices[0].delta, "content"):
content = chunk.choices[0].delta.content
if reasoning_content is not None:
if not printed_reasoning_content:
printed_reasoning_content = True
print("Start reasoning:\n", end="", flush=True)
print(reasoning_content, end="", flush=True)
if content is not None:
# Extract and print the content
if not reasoning_content and printed_reasoning_content:
answer.extend(content)
print(content, end="", flush=True)
if answer:
print("\n\n=============\nAnswer\n=============\n")
print("".join(answer))
else:
print("\n\n=============\nNo Answer\n=============\n")
print(
"No answer was generated by the model, probably because the maximum number of tokens was reached."
)
Text-Only Request
Let's do more maths and leave it up to the model to figure out how to achieve a result.
from typing import Any
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 = 262144
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) -> dict[str, Any]:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
index_begin_think = system_prompt.find("[THINK]")
index_end_think = system_prompt.find("[/THINK]")
return {
"role": "system",
"content": [
{"type": "text", "text": system_prompt[:index_begin_think]},
{
"type": "thinking",
"thinking": system_prompt[
index_begin_think + len("[THINK]") : index_end_think
],
"closed": True,
},
{
"type": "text",
"text": system_prompt[index_end_think + len("[/THINK]") :],
},
],
}
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
query = "Use each number in 2,5,6,3 exactly once, along with any combination of +, -, ×, ÷ (and parentheses for grouping), to make the number 24."
messages = [
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...:\n")
printed_reasoning_content = False
answer = []
for chunk in stream:
reasoning_content = None
content = None
# Check the content is reasoning_content or content
if hasattr(chunk.choices[0].delta, "reasoning_content"):
reasoning_content = chunk.choices[0].delta.reasoning_content
if hasattr(chunk.choices[0].delta, "content"):
content = chunk.choices[0].delta.content
if reasoning_content is not None:
if not printed_reasoning_content:
printed_reasoning_content = True
print("Start reasoning:\n", end="", flush=True)
print(reasoning_content, end="", flush=True)
if content is not None:
# Extract and print the content
if not reasoning_content and printed_reasoning_content:
answer.extend(content)
print(content, end="", flush=True)
if answer:
print("\n\n=============\nAnswer\n=============\n")
print("".join(answer))
else:
print("\n\n=============\nNo Answer\n=============\n")
print("No answer was generated by the model, probably because the maximum number of tokens was reached.")
You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.
Files
32 files · 62.0 GBWeights 969 MB (smallest of 26 GGUF variants). Sizes and sha256 from the signed manifest.
unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.mistralSubject: 
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