Instructions to use QuantFactory/Reasoning-0.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Reasoning-0.5b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Reasoning-0.5b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Reasoning-0.5b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Reasoning-0.5b-GGUF",
	filename="Reasoning-0.5b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Reasoning-0.5b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Reasoning-0.5b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Reasoning-0.5b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Reasoning-0.5b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Reasoning-0.5b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Reasoning-0.5b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Reasoning-0.5b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Reasoning-0.5b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Reasoning-0.5b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Reasoning-0.5b-GGUF-Q4_K_M

List all available models

lemonade list

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+88

-79

Files changed (1) hide show

README.md +88 -79

README.md CHANGED Viewed

@@ -1,79 +1,88 @@
----
-base_model: Qwen/Qwen2.5-0.5B-Instruct
-language:
-- en
-license: apache-2.0
-datasets:
-- KingNish/reasoning-base-20k
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
-- sft
-- reasoning
----
-[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
-# QuantFactory/Reasoning-0.5b-GGUF
-This is quantized version of [KingNish/Reasoning-0.5b](https://huggingface.co/KingNish/Reasoning-0.5b) created using llama.cpp
-# Original Model Card
-# Model Dexcription
-It's First iteration of this model. For testing purpose its just trained on 10k rows.
-It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
-It do reasoning separately no special tokens or in response reasoning.
-Below is inference code.
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-MAX_REASONING_TOKENS = 1024
-MAX_RESPONSE_TOKENS = 512
-model_name = "KingNish/Reasoning-0.5b"
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-prompt = "Which is greater 9.9 or 9.11 ??"
-messages = [
-    {"role": "user", "content": prompt}
-]
-# Generate reasoning
-reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
-reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
-reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
-reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-# print("REASONING: " + reasoning_output)
-# Generate answer
-messages.append({"role": "reasoning", "content": reasoning_output})
-response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
-response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
-response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-print("ANSWER: " + response_output)
-```
-- **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)
-- **License:** apache-2.0
-- **Finetuned from model :** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
-- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

+---
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+license: apache-2.0
+datasets:
+- KingNish/reasoning-base-20k
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+- sft
+- reasoning
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/Reasoning-0.5b-GGUF
+This is quantized version of [KingNish/Reasoning-0.5b](https://huggingface.co/KingNish/Reasoning-0.5b) created using llama.cpp
+# Original Model Card
+# Model Dexcription
+It's First iteration of this model. For testing purpose its just trained on 10k rows.
+It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
+It do reasoning separately no special tokens or in response reasoning.
+Below is inference code.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+MAX_REASONING_TOKENS = 1024
+MAX_RESPONSE_TOKENS = 512
+model_name = "KingNish/Reasoning-0.5b"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Which is greater 9.9 or 9.11 ??"
+messages = [
+    {"role": "user", "content": prompt}
+]
+# Generate reasoning
+reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
+reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+# print("REASONING: " + reasoning_output)
+# Generate answer
+messages.append({"role": "reasoning", "content": reasoning_output})
+response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
+response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print("ANSWER: " + response_output)
+```
+- **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)
+- **License:** apache-2.0
+- **Finetuned from model :** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)