Instructions to use tensorblock/L-MChat-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/L-MChat-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/L-MChat-7b-GGUF",
	filename="L-MChat-7b-Q2_K.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 tensorblock/L-MChat-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/L-MChat-7b-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/L-MChat-7b-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/L-MChat-7b-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/L-MChat-7b-GGUF:Q2_K

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 tensorblock/L-MChat-7b-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/L-MChat-7b-GGUF:Q2_K

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 tensorblock/L-MChat-7b-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/L-MChat-7b-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/L-MChat-7b-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/L-MChat-7b-GGUF with Ollama:
```
ollama run hf.co/tensorblock/L-MChat-7b-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/L-MChat-7b-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 tensorblock/L-MChat-7b-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 tensorblock/L-MChat-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/L-MChat-7b-GGUF to start chatting

Docker Model Runner
How to use tensorblock/L-MChat-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/L-MChat-7b-GGUF:Q2_K
```

Lemonade

How to use tensorblock/L-MChat-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/L-MChat-7b-GGUF:Q2_K

Run and chat with the model

lemonade run user.L-MChat-7b-GGUF-Q2_K

List all available models

lemonade list

Artples/L-MChat-7b - GGUF

This repo contains GGUF format model files for Artples/L-MChat-7b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

<s>GPT4 Correct System: {system_prompt}<|end_of_turn|>GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:

Model file specification

Filename	Quant type	File Size	Description
L-MChat-7b-Q2_K.gguf	Q2_K	2.533 GB	smallest, significant quality loss - not recommended for most purposes
L-MChat-7b-Q3_K_S.gguf	Q3_K_S	2.947 GB	very small, high quality loss
L-MChat-7b-Q3_K_M.gguf	Q3_K_M	3.277 GB	very small, high quality loss
L-MChat-7b-Q3_K_L.gguf	Q3_K_L	3.560 GB	small, substantial quality loss
L-MChat-7b-Q4_0.gguf	Q4_0	3.827 GB	legacy; small, very high quality loss - prefer using Q3_K_M
L-MChat-7b-Q4_K_S.gguf	Q4_K_S	3.856 GB	small, greater quality loss
L-MChat-7b-Q4_K_M.gguf	Q4_K_M	4.068 GB	medium, balanced quality - recommended
L-MChat-7b-Q5_0.gguf	Q5_0	4.654 GB	legacy; medium, balanced quality - prefer using Q4_K_M
L-MChat-7b-Q5_K_S.gguf	Q5_K_S	4.654 GB	large, low quality loss - recommended
L-MChat-7b-Q5_K_M.gguf	Q5_K_M	4.779 GB	large, very low quality loss - recommended
L-MChat-7b-Q6_K.gguf	Q6_K	5.534 GB	very large, extremely low quality loss
L-MChat-7b-Q8_0.gguf	Q8_0	7.167 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/L-MChat-7b-GGUF --include "L-MChat-7b-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/L-MChat-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 41

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for tensorblock/L-MChat-7b-GGUF

Base model

Artples/L-MChat-7b

Quantized

(10)

this model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

65.610
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.590
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.440
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

50.940
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.370
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

69.450
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

52.970
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

24.200
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

7.930
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

7.380
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

8.120
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

25.540