Instructions to use ByteBrew23/C_ASM_mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteBrew23/C_ASM_mistral with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ByteBrew23/C_ASM_mistral",
	filename="Casmmistq5km.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 ByteBrew23/C_ASM_mistral with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ByteBrew23/C_ASM_mistral
# Run inference directly in the terminal:
llama-cli -hf ByteBrew23/C_ASM_mistral

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ByteBrew23/C_ASM_mistral
# Run inference directly in the terminal:
llama-cli -hf ByteBrew23/C_ASM_mistral

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 ByteBrew23/C_ASM_mistral
# Run inference directly in the terminal:
./llama-cli -hf ByteBrew23/C_ASM_mistral

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 ByteBrew23/C_ASM_mistral
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ByteBrew23/C_ASM_mistral

Use Docker

docker model run hf.co/ByteBrew23/C_ASM_mistral

LM Studio
Jan
Ollama
How to use ByteBrew23/C_ASM_mistral with Ollama:
```
ollama run hf.co/ByteBrew23/C_ASM_mistral
```

Unsloth Studio new

How to use ByteBrew23/C_ASM_mistral 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 ByteBrew23/C_ASM_mistral 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 ByteBrew23/C_ASM_mistral to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ByteBrew23/C_ASM_mistral to start chatting

Docker Model Runner
How to use ByteBrew23/C_ASM_mistral with Docker Model Runner:
```
docker model run hf.co/ByteBrew23/C_ASM_mistral
```

Lemonade

How to use ByteBrew23/C_ASM_mistral with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ByteBrew23/C_ASM_mistral

Run and chat with the model

lemonade run user.C_ASM_mistral-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

Instruction:

{}

Input:

{}

Response:

{}"""

Finetune of mistral v0.3, used C and ASM data from codebagel. Used https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_ for training, didn't change the amount of training epochs or whatever it's called.

Downloads last month: 2

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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