Reinforcement Learning
Transformers
GGUF
English
code
structural-engineering
openseespy
scientific-modeling
grpo
autobm
conversational
Instructions to use mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/AutoBM-Seed-Coder-8B-R-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/AutoBM-Seed-Coder-8B-R-GGUF", filename="AutoBM-Seed-Coder-8B-R.IQ4_XS.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 mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/AutoBM-Seed-Coder-8B-R-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 mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/AutoBM-Seed-Coder-8B-R-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 mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/AutoBM-Seed-Coder-8B-R-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 mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with Ollama:
ollama run hf.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M
- Unsloth Studio new
How to use mradermacher/AutoBM-Seed-Coder-8B-R-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 mradermacher/AutoBM-Seed-Coder-8B-R-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 mradermacher/AutoBM-Seed-Coder-8B-R-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/AutoBM-Seed-Coder-8B-R-GGUF to start chatting
- Docker Model Runner
How to use mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/AutoBM-Seed-Coder-8B-R-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/AutoBM-Seed-Coder-8B-R-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AutoBM-Seed-Coder-8B-R-GGUF-Q4_K_M
List all available models
lemonade list
File size: 3,925 Bytes
c3bcd9d e0f7ef6 c3bcd9d 348c72b c3bcd9d 348c72b c3bcd9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
base_model: yongqiqng/AutoBM-Seed-Coder-8B-R
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- code
- structural-engineering
- openseespy
- scientific-modeling
- reinforcement-learning
- grpo
- autobm
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/yongqiqng/AutoBM-Seed-Coder-8B-R
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#AutoBM-Seed-Coder-8B-R-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q3_K_M.gguf) | Q3_K_M | 4.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q3_K_L.gguf) | Q3_K_L | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q4_K_M.gguf) | Q4_K_M | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q5_K_S.gguf) | Q5_K_S | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q6_K.gguf) | Q6_K | 6.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.Q8_0.gguf) | Q8_0 | 8.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/AutoBM-Seed-Coder-8B-R-GGUF/resolve/main/AutoBM-Seed-Coder-8B-R.f16.gguf) | f16 | 16.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
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