Instructions to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF",
	filename="VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-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 tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K

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 tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K

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 tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-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 tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-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 tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF to start chatting

Docker Model Runner
How to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF:Q2_K

Run and chat with the model

lemonade run user.Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF-Q2_K

List all available models

lemonade list

Nellyw888/VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb - GGUF

Join our Discord to learn more about what we're building ↗

This repo contains GGUF format model files for Nellyw888/VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

Our projects

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Prompt template

<s>[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]

Model file specification

Filename	Quant type	File Size	Description
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q2_K.gguf	Q2_K	2.533 GB	smallest, significant quality loss - not recommended for most purposes
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q3_K_S.gguf	Q3_K_S	2.948 GB	very small, high quality loss
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q3_K_M.gguf	Q3_K_M	3.298 GB	very small, high quality loss
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q3_K_L.gguf	Q3_K_L	3.597 GB	small, substantial quality loss
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q4_0.gguf	Q4_0	3.826 GB	legacy; small, very high quality loss - prefer using Q3_K_M
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q4_K_S.gguf	Q4_K_S	3.857 GB	small, greater quality loss
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q4_K_M.gguf	Q4_K_M	4.081 GB	medium, balanced quality - recommended
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q5_0.gguf	Q5_0	4.652 GB	legacy; medium, balanced quality - prefer using Q4_K_M
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q5_K_S.gguf	Q5_K_S	4.652 GB	large, low quality loss - recommended
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q5_K_M.gguf	Q5_K_M	4.783 GB	large, very low quality loss - recommended
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q6_K.gguf	Q6_K	5.529 GB	very large, extremely low quality loss
VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q8_0.gguf	Q8_0	7.161 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF --include "VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 40

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

Video Preview

Reinforcement Learning

Model tree for tensorblock/Nellyw888_VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb-GGUF

Base model

Nellyw888/VeriReason-codeLlama-7b-RTLCoder-Verilog-GRPO-reasoning-tb

Quantized

(2)

this model