COMMERCIAL USE
Collection
7 items • Updated • 1
How to use koesn/Garten2-7B-GGUF with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("koesn/Garten2-7B-GGUF", device_map="auto")How to use koesn/Garten2-7B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf koesn/Garten2-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf koesn/Garten2-7B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf koesn/Garten2-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf koesn/Garten2-7B-GGUF:Q4_K_M
# 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 koesn/Garten2-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf koesn/Garten2-7B-GGUF:Q4_K_M
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 koesn/Garten2-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf koesn/Garten2-7B-GGUF:Q4_K_M
docker model run hf.co/koesn/Garten2-7B-GGUF:Q4_K_M
How to use koesn/Garten2-7B-GGUF with Ollama:
ollama run hf.co/koesn/Garten2-7B-GGUF:Q4_K_M
How to use koesn/Garten2-7B-GGUF with Docker Model Runner:
docker model run hf.co/koesn/Garten2-7B-GGUF:Q4_K_M
How to use koesn/Garten2-7B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull koesn/Garten2-7B-GGUF:Q4_K_M
lemonade run user.Garten2-7B-GGUF-Q4_K_M
lemonade list
This repo contains GGUF format model files for Garten2-7B-GGUF.
| Name | Quant | Bits | File Size | Remark |
|---|---|---|---|---|
| garten2-7b.IQ3_XXS.gguf | IQ3_XXS | 3 | 3.02 GB | 3.06 bpw quantization |
| garten2-7b.IQ3_S.gguf | IQ3_S | 3 | 3.18 GB | 3.44 bpw quantization |
| garten2-7b.IQ3_M.gguf | IQ3_M | 3 | 3.28 GB | 3.66 bpw quantization mix |
| garten2-7b.Q4_0.gguf | Q4_0 | 4 | 4.11 GB | 3.56G, +0.2166 ppl |
| garten2-7b.IQ4_NL.gguf | IQ4_NL | 4 | 4.16 GB | 4.25 bpw non-linear quantization |
| garten2-7b.Q4_K_M.gguf | Q4_K_M | 4 | 4.37 GB | 3.80G, +0.0532 ppl |
| garten2-7b.Q5_K_M.gguf | Q5_K_M | 5 | 5.13 GB | 4.45G, +0.0122 ppl |
| garten2-7b.Q6_K.gguf | Q6_K | 6 | 5.94 GB | 5.15G, +0.0008 ppl |
| garten2-7b.Q8_0.gguf | Q8_0 | 8 | 7.70 GB | 6.70G, +0.0004 ppl |
| path | type | architecture | rope_theta | sliding_win | max_pos_embed |
|---|---|---|---|---|---|
| senseable/Garten2-7B | mistral | MistralForCausalLM | 10000.0 | 4096 | 32768 |
Introducing Garten2-7B, a cutting-edge, small 7B all-purpose Language Model (LLM), designed to redefine the boundaries of artificial intelligence in natural language understanding and generation. Garten2-7B stands out with its unique architecture, expertly crafted to deliver exceptional performance in a wide array of tasks, from conversation to content creation.
3-bit
4-bit
5-bit
6-bit
8-bit
Base model
mistralai/Mistral-7B-v0.1