Instructions to use RichardErkhov/double7_-_vicuna-68m-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/double7_-_vicuna-68m-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/double7_-_vicuna-68m-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 RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/double7_-_vicuna-68m-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 RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/double7_-_vicuna-68m-gguf with Ollama:
ollama run hf.co/RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/double7_-_vicuna-68m-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/double7_-_vicuna-68m-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/double7_-_vicuna-68m-gguf:Q4_K_M
Run and chat with the model
lemonade run user.double7_-_vicuna-68m-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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Check out the documentation for more information.
Quantization made by Richard Erkhov.
vicuna-68m - GGUF
- Model creator: https://huggingface.co/double7/
- Original model: https://huggingface.co/double7/vicuna-68m/
| Name | Quant method | Size |
|---|---|---|
| vicuna-68m.Q2_K.gguf | Q2_K | 0.03GB |
| vicuna-68m.IQ3_XS.gguf | IQ3_XS | 0.04GB |
| vicuna-68m.IQ3_S.gguf | IQ3_S | 0.04GB |
| vicuna-68m.Q3_K_S.gguf | Q3_K_S | 0.04GB |
| vicuna-68m.IQ3_M.gguf | IQ3_M | 0.04GB |
| vicuna-68m.Q3_K.gguf | Q3_K | 0.04GB |
| vicuna-68m.Q3_K_M.gguf | Q3_K_M | 0.04GB |
| vicuna-68m.Q3_K_L.gguf | Q3_K_L | 0.04GB |
| vicuna-68m.IQ4_XS.gguf | IQ4_XS | 0.04GB |
| vicuna-68m.Q4_0.gguf | Q4_0 | 0.04GB |
| vicuna-68m.IQ4_NL.gguf | IQ4_NL | 0.04GB |
| vicuna-68m.Q4_K_S.gguf | Q4_K_S | 0.04GB |
| vicuna-68m.Q4_K.gguf | Q4_K | 0.04GB |
| vicuna-68m.Q4_K_M.gguf | Q4_K_M | 0.04GB |
| vicuna-68m.Q4_1.gguf | Q4_1 | 0.04GB |
| vicuna-68m.Q5_0.gguf | Q5_0 | 0.05GB |
| vicuna-68m.Q5_K_S.gguf | Q5_K_S | 0.05GB |
| vicuna-68m.Q5_K.gguf | Q5_K | 0.05GB |
| vicuna-68m.Q5_K_M.gguf | Q5_K_M | 0.05GB |
| vicuna-68m.Q5_1.gguf | Q5_1 | 0.05GB |
| vicuna-68m.Q6_K.gguf | Q6_K | 0.05GB |
| vicuna-68m.Q8_0.gguf | Q8_0 | 0.07GB |
Original model description:
license: apache-2.0 datasets: - anon8231489123/ShareGPT_Vicuna_unfiltered language: - en pipeline_tag: text-generation
Model description
This is a Vicuna-like model with only 68M parameters, which is fine-tuned from LLaMA-68m on ShareGPT data.
The training setup follows the Vicuna suite.
The model is mainly developed as a base Small Speculative Model in the MCSD paper. As a comparison, it can be better aligned to the Vicuna models than LLaMA-68m with little loss of alignment to the LLaMA models.
| Draft Model | Target Model | Alignment |
|---|---|---|
| LLaMA-68/160M | LLaMA-13/33B | π |
| LLaMA-68/160M | Vicuna-13/33B | π |
| Vicuna-68/160M | LLaMA-13/33B | π |
| Vicuna-68/160M | Vicuna-13/33B | π |
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