Instructions to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF",
	filename="DevQuasar-R1-Uncensored-Llama-8B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-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 DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-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 DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-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 DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M

Ollama
How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with Ollama:
```
ollama run hf.co/DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-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 DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-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 DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF to start chatting

Docker Model Runner
How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M
```

Lemonade

How to use DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DevQuasar-R1-Uncensored-Llama-8B-GGUF-Q4_K_M

List all available models

lemonade list

'Make knowledge free for everyone'

DevQuasar-R1-Uncensored-Llama-8B

Eval results

hf (pretrained=DevQuasar/DevQuasar-R1-Uncensored-Llama-8B,parallelize=True,dtype=float16), gen_kwargs: (temperature=0.6,top_p=0.95,do_sample=True), limit: None, num_fewshot: None, batch_size: auto:4 (1,16,64,64)

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
hellaswag	1	none	0	acc	↑	0.6052	±	0.0049
		none	0	acc_norm	↑	0.8021	±	0.0040
leaderboard_bbh	N/A
- leaderboard_bbh_boolean_expressions	1	none	3	acc_norm	↑	0.8360	±	0.0235
- leaderboard_bbh_causal_judgement	1	none	3	acc_norm	↑	0.6043	±	0.0359
- leaderboard_bbh_date_understanding	1	none	3	acc_norm	↑	0.4840	±	0.0317
- leaderboard_bbh_disambiguation_qa	1	none	3	acc_norm	↑	0.6360	±	0.0305
- leaderboard_bbh_formal_fallacies	1	none	3	acc_norm	↑	0.5680	±	0.0314
- leaderboard_bbh_geometric_shapes	1	none	3	acc_norm	↑	0.2760	±	0.0283
- leaderboard_bbh_hyperbaton	1	none	3	acc_norm	↑	0.5440	±	0.0316
- leaderboard_bbh_logical_deduction_five_objects	1	none	3	acc_norm	↑	0.4320	±	0.0314
- leaderboard_bbh_logical_deduction_seven_objects	1	none	3	acc_norm	↑	0.4640	±	0.0316
- leaderboard_bbh_logical_deduction_three_objects	1	none	3	acc_norm	↑	0.6440	±	0.0303
- leaderboard_bbh_movie_recommendation	1	none	3	acc_norm	↑	0.7600	±	0.0271
- leaderboard_bbh_navigate	1	none	3	acc_norm	↑	0.6240	±	0.0307
- leaderboard_bbh_object_counting	1	none	3	acc_norm	↑	0.5440	±	0.0316
- leaderboard_bbh_penguins_in_a_table	1	none	3	acc_norm	↑	0.4658	±	0.0414
- leaderboard_bbh_reasoning_about_colored_objects	1	none	3	acc_norm	↑	0.5640	±	0.0314
- leaderboard_bbh_ruin_names	1	none	3	acc_norm	↑	0.7160	±	0.0286
- leaderboard_bbh_salient_translation_error_detection	1	none	3	acc_norm	↑	0.4920	±	0.0317
- leaderboard_bbh_snarks	1	none	3	acc_norm	↑	0.5899	±	0.0370
- leaderboard_bbh_sports_understanding	1	none	3	acc_norm	↑	0.6880	±	0.0294
- leaderboard_bbh_temporal_sequences	1	none	3	acc_norm	↑	0.2200	±	0.0263
- leaderboard_bbh_tracking_shuffled_objects_five_objects	1	none	3	acc_norm	↑	0.1880	±	0.0248
- leaderboard_bbh_tracking_shuffled_objects_seven_objects	1	none	3	acc_norm	↑	0.1320	±	0.0215
- leaderboard_bbh_tracking_shuffled_objects_three_objects	1	none	3	acc_norm	↑	0.3040	±	0.0292
- leaderboard_bbh_web_of_lies	1	none	3	acc_norm	↑	0.4760	±	0.0316
leaderboard_gpqa	N/A
- leaderboard_gpqa_diamond	1	none	0	acc_norm	↑	0.3232	±	0.0333
- leaderboard_gpqa_extended	1	none	0	acc_norm	↑	0.3498	±	0.0204
- leaderboard_gpqa_main	1	none	0	acc_norm	↑	0.3527	±	0.0226
leaderboard_ifeval	3	none	0	inst_level_loose_acc	↑	0.4628	±	N/A
		none	0	inst_level_strict_acc	↑	0.4365	±	N/A
		none	0	prompt_level_loose_acc	↑	0.3216	±	0.0201
		none	0	prompt_level_strict_acc	↑	0.2902	±	0.0195
leaderboard_math_hard	N/A
- leaderboard_math_algebra_hard	2	none	4	exact_match	↑	0.5798	±	0.0282
- leaderboard_math_counting_and_prob_hard	2	none	4	exact_match	↑	0.2276	±	0.0380
- leaderboard_math_geometry_hard	2	none	4	exact_match	↑	0.1970	±	0.0347
- leaderboard_math_intermediate_algebra_hard	2	none	4	exact_match	↑	0.1036	±	0.0182
- leaderboard_math_num_theory_hard	2	none	4	exact_match	↑	0.3377	±	0.0382
- leaderboard_math_prealgebra_hard	2	none	4	exact_match	↑	0.4715	±	0.0360
- leaderboard_math_precalculus_hard	2	none	4	exact_match	↑	0.1111	±	0.0271
leaderboard_mmlu_pro	0.1	none	5	acc	↑	0.3608	±	0.0044
leaderboard_musr	N/A
- leaderboard_musr_murder_mysteries	1	none	0	acc_norm	↑	0.5920	±	0.0311
- leaderboard_musr_object_placements	1	none	0	acc_norm	↑	0.3867	±	0.0305
- leaderboard_musr_team_allocation	1	none	0	acc_norm	↑	0.3560	±	0.0303

Compare to base DeepSeek-R1-Distill-Llama-8B

Model shows improvements in most if these tests:

Link to eval results

DevQuasar-R1-Uncensored-Llama-8B DeepSeek-R1-Distill-Llama-8B

Downloads last month: 111

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for DevQuasar-4/DevQuasar-R1-Uncensored-Llama-8B-GGUF

Base model

DevQuasar-3/DevQuasar-R1-Uncensored-Llama-8B

Quantized

(3)

this model