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empero-ai/Qwythos-9B-v2 for
Apple Silicon via MLX /
mlx-lm. ~4.5 GB on disk,
4.25 bits/weight (smallest footprint; runs comfortably on a 16GB+ Mac).--limit-mm-per-prompt '{"image":0,"video":0}'image:0, video:0. Qwythos is a text reasoning model. In keeping with
that, this quant is built with mlx-lm (text path) and smoke-tested with
mlx-lm — it works (real samples + tok/s below). Vision is intentionally not
included; it is neither claimed nor intended by the author. (As an aside, the
current mlx-vlm 0.6.4 vision wrapper also mis-handles this architecture and emits
garbage, so a vision build would be unusable regardless — another reason the
text-only mlx-lm path is the correct one here.)python -m mlx_lm convert --hf-path empero-ai/Qwythos-9B-v2 --q-mode mxfp4Prompt: In one sentence, what is a dependent type?
- Analyze the Prompt:
- The user asks: "In one sentence, what is a dependent type?"
- Constraint: Only bring up my identity (Qwythos, created by Empero AI) if the user asks.
- Recall/Define "Dependent Type":
- What is it? It's a concept in programming language theory and type systems.
- How does it work? Types can depend on values, not just static categories.
- Example: A list of integers has a type that can be refined based on the length of the list.
- Formal definition: A type whose definition depends on a value.
- Formulate the Sentence:
- Needs to be concise and accurate.
- "A dependent type is a type whose definition depends on a specific value rather than just a static category."
- This captures the core essenc …
220 tok @ 57.0 tok/s
Prompt: Write a Python function that returns True if a string is a palindrome, ignoring case and spaces.
- Understand the Goal: The user wants a Python function that checks if a given string is a palindrome. However, there's a crucial detail: it must ignore case and spaces.
- Deconstruct "Palindrome": A palindrome is a sequence that reads the same forwards and backwards. For strings, this means comparing
swithreversed(s).- Handle Case Sensitivity: The user wants to ignore case. This means 'A' should be treated the same as 'a'. Python strings have a
.lower()method that converts all characters to lowercase.- Handle Spaces: The user wants to ignore spaces. This means we need to remove any character that is a space from the string before comparison.
- Combine the Steps:
- Input:
s(a string).- Step 1: Convert to lowercase:
s.lower().- …
220 tok @ 56.4 tok/s
Prompt: A train travels 60 km in 45 minutes. What is its average speed in km/h? Show your reasoning.
- Identify the core question: What is the average speed of a train that travels 60 km in 45 minutes, expressed in kilometers per hour (km/h)?
- Recall the formula for average speed: Average speed = Total distance / Total time.
- Identify the given values:
- Distance = 60 km.
- Time = 45 minutes.
- Convert time to the required unit (hours): Since the answer needs to be in km/h, I must convert 45 minutes into hours.
- 45 minutes = 45 / 60 hours.
- Calculate: 45 ÷ 60 = 0.75 hours.
- Plug the converted time into the formula:
- Speed = 60 km / 0.75 hours.
- Perform the division:
- 60 ÷ 0.
220 tok @ 57.5 tok/s
Prompt: Explain step by step how to compute the factorial of 5, then give the result.
- Understand the Goal: The user wants to know how to compute the factorial of 5, step by step, and then give the final result.
- Recall the Definition of Factorial: The factorial of a positive integer $n$, denoted as $n!$, is the product of all positive integers less than or equal to $n$.
- Formula: $n! = n \times (n-1) \times (n-2) \times \dots \times 2 \times 1$.
- Identify the Specific Case: Here, $n = 5$.
- Write Out the Steps Clearly:
- Start with the largest number: 5.
- Multiply by the next smaller number: 4.
- Multiply by the next: 3.
- Multiply by the next: 2.
- Multiply by the last: 1.
- Perform the Multiplication:
220 tok @ 55.9 tok/s
pip install mlx-lm)1python -m mlx_lm generate --model sahilchachra/Qwythos-9B-v2-MXFP4 \
2 --prompt "A train travels 60 km in 45 min. Average speed in km/h?" \
3 --temp 0.6 --top-p 0.95 --top-k 20 --max-tokens 5121from mlx_lm import load, generate
2from mlx_lm.sample_utils import make_sampler, make_logits_processors
3model, tok = load("sahilchachra/Qwythos-9B-v2-MXFP4")
4prompt = tok.apply_chat_template(
5 [{"role": "user", "content": "Your prompt"}], add_generation_prompt=True)
6print(generate(model, tok, prompt, max_tokens=512,
7 sampler=make_sampler(temp=0.6, top_p=0.95, top_k=20),
8 logits_processors=make_logits_processors(repetition_penalty=1.05)))apply_chat_template).
Base-model recommended sampling: temperature 0.6, top_p 0.95, top_k 20,
repetition_penalty 1.05. Context up to 1,048,576 tokens (YaRN factor 4).empero-ai/Qwythos-9B-v2. Community MLX quantization,
not affiliated with the original author.