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

Libraries

How to use xavierwoon/cesterllama with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xavierwoon/cesterllama")
model = AutoModelForCausalLM.from_pretrained("xavierwoon/cesterllama")

llama-cpp-python

How to use xavierwoon/cesterllama with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xavierwoon/cesterllama",
	filename="unsloth.F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use xavierwoon/cesterllama with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use xavierwoon/cesterllama with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use xavierwoon/cesterllama 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/cesterllama" \
    --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/cesterllama",
		"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/cesterllama" \
        --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/cesterllama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Unsloth Studio new

How to use xavierwoon/cesterllama 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/cesterllama 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/cesterllama to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use xavierwoon/cesterllama with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.cesterllama-F16

List all available models

lemonade list

xavierwoon commited on Oct 8, 2024

Commit

7bfa745

verified ·

1 Parent(s): 5e97610

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@@ -20,21 +20,18 @@ tags:
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
@@ -44,107 +41,112 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### 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]
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
 ### Testing Data, Factors & Metrics
 #### Testing Data
 <!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
 ### Results
-[More Information Needed]
 #### Summary
-## Model Examination [optional]
 <!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 - **Hardware Type:** [More Information Needed]
@@ -173,7 +175,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
@@ -185,7 +187,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 [More Information Needed]
@@ -199,4 +201,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Model Card Contact
-[More Information Needed]

 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Xavier Woon
+- **Shared by:** Xavier
+- **Model type:** Text Generation
+- **Finetuned from model [optional]:** Llama 3 8B
+<!-- ### Model Sources [optional] -->
 <!-- Provide the basic links for the model. -->
+<!--- **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed] -->
 ## Uses
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<!-- [More Information Needed] -->
+<!-- ### Downstream Use [optional] -->
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+<!-- [More Information Needed] -->
+<!-- ### Out-of-Scope Use -->
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+<!-- [More Information Needed] -->
 ## Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+<!-- [More Information Needed] -->
+<!-- ### Recommendations -->
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+<!-- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. -->
+<!-- ## How to Get Started with the Model -->
+<!-- Use the code below to get started with the model. -->
+<!-- [More Information Needed] -->
 ## Training Details
+Trained with 3 epochs.
 ### Training Data
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+<!-- [More Information Needed] -->
+<!-- ### Training Procedure -->
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+<!-- #### Preprocessing [optional] -->
+<!-- [More Information Needed] -->
+<!-- #### 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] -->
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+<!-- [More Information Needed] -->
+<!-- ## Evaluation -->
 <!-- This section describes the evaluation protocols and provides the results. -->
 ### Testing Data, Factors & Metrics
+Code coverage and Robustness.
 #### Testing Data
 <!-- This should link to a Dataset Card if possible. -->
+<!-- [More Information Needed] -->
+<!-- #### Factors -->
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+<!-- [More Information Needed] -->
+<!-- #### Metrics -->
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
+<!-- [More Information Needed] -->
 ### Results
+<!-- [More Information Needed] -->
 #### Summary
+<!-- ## Model Examination [optional] -->
 <!-- Relevant interpretability work for the model goes here -->
+<!-- [More Information Needed] -->
+<!-- ## Environmental Impact -->
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+<!--
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 - **Hardware Type:** [More Information Needed]
 ## Citation [optional]
+If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.
 **BibTeX:**
 ## Glossary [optional]
+If relevant, include terms and calculations in this section that can help readers understand the model or model card.
 [More Information Needed]
 ## Model Card Contact
+[More Information Needed] -->