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

Libraries

How to use xavierwoon/cestermistral with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="xavierwoon/cestermistral")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xavierwoon/cestermistral")
model = AutoModelForCausalLM.from_pretrained("xavierwoon/cestermistral", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use xavierwoon/cestermistral 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 xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
llama cli -hf xavierwoon/cestermistral:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
llama cli -hf xavierwoon/cestermistral:F16

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 xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
./llama-cli -hf xavierwoon/cestermistral:F16

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 xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xavierwoon/cestermistral:F16

Use Docker

docker model run hf.co/xavierwoon/cestermistral:F16

LM Studio
Jan

vLLM

How to use xavierwoon/cestermistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xavierwoon/cestermistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cestermistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xavierwoon/cestermistral:F16

SGLang

How to use xavierwoon/cestermistral with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "xavierwoon/cestermistral" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cestermistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "xavierwoon/cestermistral" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cestermistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use xavierwoon/cestermistral with Ollama:
```
ollama run hf.co/xavierwoon/cestermistral:F16
```

Unsloth Studio

How to use xavierwoon/cestermistral 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 xavierwoon/cestermistral 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 xavierwoon/cestermistral to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use xavierwoon/cestermistral with Docker Model Runner:
```
docker model run hf.co/xavierwoon/cestermistral:F16
```

Lemonade

How to use xavierwoon/cestermistral with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xavierwoon/cestermistral:F16

Run and chat with the model

lemonade run user.cestermistral-F16

List all available models

lemonade list

Atomic Chat

xavierwoon commited on Nov 12, 2024

Commit

c23ddea

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1 Parent(s): 55a2735

Update README.md

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README.md +7 -7

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ base_model:
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
@@ -28,8 +28,8 @@ base_model:
 <!-- - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed] -->
 - **Model type:** Mistral
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
 - **Finetuned from model [optional]:** unsloth/mistral-7b-bnb-4bit
 <!-- ### Model Sources [optional]
@@ -44,11 +44,11 @@ Provide the basic links for the model.
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
 <!-- ### Downstream Use [optional] -->
@@ -113,7 +113,7 @@ text_streamer = TextStreamer(tokenizer)
 _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
 ```
-[More Information Needed]
 ## Training Details
@@ -138,7 +138,7 @@ Training Data was created based on Data Structures and Algorithm (DSA) codes cre
 <!-- #### Training Hyperparameters -->
-<!-- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> -->
 <!-- #### Speeds, Sizes, Times [optional] -->

 <!-- Provide a quick summary of what the model is/does. -->
+Cestermistral is a fine-tuned Mistral 7B model that is able to generate Libcester unit test cases in the correct format.
 ## Model Details
 <!-- - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed] -->
 - **Model type:** Mistral
+<!-- - **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed] -->
 - **Finetuned from model [optional]:** unsloth/mistral-7b-bnb-4bit
 <!-- ### Model Sources [optional]
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+<!-- ### Direct Use -->
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<!-- [More Information Needed] -->
 <!-- ### Downstream Use [optional] -->
 _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
 ```
+<!-- [More Information Needed] -->
 ## Training Details
 <!-- #### Training Hyperparameters -->
+<!-- - **Training regime:** [More Information Needed] fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 <!-- #### Speeds, Sizes, Times [optional] -->