Instructions to use QuantFactory/bigstral-12b-32k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/bigstral-12b-32k-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/bigstral-12b-32k-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/bigstral-12b-32k-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/bigstral-12b-32k-GGUF", filename="bigstral-12b-32k.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/bigstral-12b-32k-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/bigstral-12b-32k-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 QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/bigstral-12b-32k-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 QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/bigstral-12b-32k-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 QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/bigstral-12b-32k-GGUF with Ollama:
ollama run hf.co/QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/bigstral-12b-32k-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 QuantFactory/bigstral-12b-32k-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 QuantFactory/bigstral-12b-32k-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/bigstral-12b-32k-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/bigstral-12b-32k-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/bigstral-12b-32k-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/bigstral-12b-32k-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.bigstral-12b-32k-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/bigstral-12b-32k-GGUF
This is quantized version of abacusai/bigstral-12b-32k created using llama.cpp
Original Model Card
bigstral-12b-32k
ollama run ehartford/bigstral
I reached for SOLAR but realized that its only 4k... Decided to make my own 32k version.
This is mistralai/Mistral-7B-Instruct-v0.2 with layers interleaved to create a larger 12b model.
This is a merge of pre-trained language models created using mergekit.
Prompt Format
<s>[INST] What is your favourite condiment? [/INST]
Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s>
[INST] Do you have dangerously spicy mayonnaise recipes? [/INST]
Example Output
give me some code that is very simple in javascript but would be very difficult in ANSI-C
In JavaScript, functions are first-class values, which means you can assign them to variables, pass them as arguments to other functions, and even nest them inside other functions. This leads to concise and expressive code.
Here's an example of a simple recursive function that calculates the factorial of a given number in JavaScript:
const factorial = n => n === 0 ? 1 : n * factorial(n - 1); console.log(factorial(5)); // Output: 120Now, let's try to write an equivalent code snippet in ANSI-C with similar functionality:
#include <stdio.h> #define MAX_RECURSION_DEPTH 1000 int factorial(int n) { if (n == 0) return 1; else { int result; result = factorial(--n); if (++depth > MAX_RECURSION_DEPTH) while(1){} // Recursion stack overflow return n * result; } } int main() { int i, result; int n = 5; int depth = 0; result = factorial(n); printf("Factorial of %d is: %d\n", n, result); return 0; }As you can see, the ANSI-C code becomes more verbose and has several disadvantages compared to JavaScript:
- Need to define a maximum recursion depth using a constant or an array index, which can lead to hardcoded limits and potential stack overflow issues.
- The use of preprocessor directives (#define) for defining functions and macros can make the code less readable and harder to understand.
- The lack of closures in ANSI-C requires using static variables or passing arguments as arguments to nested functions, making the code more verbose and less concise.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 8]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [4, 12]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [8, 16]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [12, 20]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [16, 24]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [20, 28]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [24, 32]
model: mistralai/Mistral-7B-Instruct-v0.2
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 18.05 |
| IFEval (0-Shot) | 41.94 |
| BBH (3-Shot) | 25.56 |
| MATH Lvl 5 (4-Shot) | 0.98 |
| GPQA (0-shot) | 5.70 |
| MuSR (0-shot) | 15.86 |
| MMLU-PRO (5-shot) | 18.24 |
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Model tree for QuantFactory/bigstral-12b-32k-GGUF
Base model
mistralai/Mistral-7B-Instruct-v0.2Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard41.940
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard25.560
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard0.980
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.700
- acc_norm on MuSR (0-shot)Open LLM Leaderboard15.860
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard18.240
ollama run hf.co/QuantFactory/bigstral-12b-32k-GGUF: