Instructions to use prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF

SGLang

How to use prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF
```

DeepSeek-R1-Llama-8B-F32-GGUF

DeepSeek-R1-Llama-8B-F32-GGUF is a quantized version of DeepSeek-R1-Distill-Llama-8B, trained using reinforcement learning (RL) directly on the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach enables the model to explore chain-of-thought (CoT) reasoning for solving complex problems, leading to the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating extended chains of thought

Model File

File Name	Size	Format	Notes
DeepSeek-R1-Llama-8B.BF16.gguf	15.6 GB	GGUF	BF16 precision model
DeepSeek-R1-Llama-8B.F16.gguf	16.1 GB	GGUF	FP16 precision model
DeepSeek-R1-Llama-8B.F32.gguf	32.1 GB	GGUF	FP32 precision model
.gitattributes	1.75 kB	Text	Git LFS tracking config
config.json	31 B	JSON	Model configuration file
README.md	767 B	Markdown	This readme file

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 25

GGUF

Hardware compatibility

16-bit

32-bit

Model tree for prithivMLmods/DeepSeek-R1-Llama-8B-F32-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Quantized

(191)

this model