Sentence Similarity
sentence-transformers
GGUF
feature-extraction
Trained with AutoTrain
llama-cpp
gguf-my-repo
conversational
Instructions to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF") sentences = [ "search_query: i love autotrain", "search_query: huggingface auto train", "search_query: hugging face auto train", "search_query: i love autotrain" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-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 unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
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 unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
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 unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF with Ollama:
ollama run hf.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
- Lemonade
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
File size: 2,386 Bytes
fff1cb4 | 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 | ---
base_model: unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
datasets:
- skratos115/opendevin_DataDevinator
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- autotrain
- llama-cpp
- gguf-my-repo
widget:
- source_sentence: 'search_query: i love autotrain'
sentences:
- 'search_query: huggingface auto train'
- 'search_query: hugging face auto train'
- 'search_query: i love autotrain'
---
# unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF
This model was converted to GGUF format from [`unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2`](https://huggingface.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF --hf-file smollm-135m-instruct-devinator-v0.2-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF --hf-file smollm-135m-instruct-devinator-v0.2-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF --hf-file smollm-135m-instruct-devinator-v0.2-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2-Q8_0-GGUF --hf-file smollm-135m-instruct-devinator-v0.2-q8_0.gguf -c 2048
```
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