Instructions to use Wade5/MyModel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wade5/MyModel2 with Transformers:

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

pipe = pipeline("text-generation", model="Wade5/MyModel2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wade5/MyModel2")
model = AutoModelForCausalLM.from_pretrained("Wade5/MyModel2")

llama-cpp-python

How to use Wade5/MyModel2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Wade5/MyModel2",
	filename="first.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Wade5/MyModel2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Wade5/MyModel2
# Run inference directly in the terminal:
llama-cli -hf Wade5/MyModel2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Wade5/MyModel2
# Run inference directly in the terminal:
llama-cli -hf Wade5/MyModel2

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 Wade5/MyModel2
# Run inference directly in the terminal:
./llama-cli -hf Wade5/MyModel2

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 Wade5/MyModel2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Wade5/MyModel2

Use Docker

docker model run hf.co/Wade5/MyModel2

LM Studio
Jan

vLLM

How to use Wade5/MyModel2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wade5/MyModel2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wade5/MyModel2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Wade5/MyModel2

SGLang

How to use Wade5/MyModel2 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 "Wade5/MyModel2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wade5/MyModel2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Wade5/MyModel2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wade5/MyModel2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Wade5/MyModel2 with Ollama:
```
ollama run hf.co/Wade5/MyModel2
```

Unsloth Studio new

How to use Wade5/MyModel2 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 Wade5/MyModel2 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 Wade5/MyModel2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Wade5/MyModel2 to start chatting

Docker Model Runner
How to use Wade5/MyModel2 with Docker Model Runner:
```
docker model run hf.co/Wade5/MyModel2
```

Lemonade

How to use Wade5/MyModel2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Wade5/MyModel2

Run and chat with the model

lemonade run user.MyModel2-{{QUANT_TAG}}

List all available models

lemonade list

Wade5 commited on Feb 13, 2025

Commit

d74e26a

verified ·

1 Parent(s): 29e0512

End of training

Browse files

Files changed (2) hide show

README.md +76 -3
generation_config.json +9 -0

README.md CHANGED Viewed

@@ -1,3 +1,76 @@
----
-license: unknown
----

+---
+library_name: transformers
+license: mit
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+tags:
+- generated_from_trainer
+model-index:
+- name: MyModel2
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# MyModel2
+This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1089
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.9498        | 0.2693 | 500  | 0.6119          |
+| 0.6245        | 0.5385 | 1000 | 0.5831          |
+| 0.5931        | 0.8078 | 1500 | 0.5462          |
+| 0.561         | 1.0770 | 2000 | 0.5148          |
+| 0.5312        | 1.3463 | 2500 | 0.4750          |
+| 0.523         | 1.6155 | 3000 | 0.4421          |
+| 0.5121        | 1.8848 | 3500 | 0.4096          |
+| 0.4059        | 2.1540 | 4000 | 0.3263          |
+| 0.3559        | 2.4233 | 4500 | 0.2780          |
+| 0.3409        | 2.6925 | 5000 | 0.2367          |
+| 0.3352        | 2.9618 | 5500 | 0.1973          |
+| 0.1918        | 3.2310 | 6000 | 0.1652          |
+| 0.1826        | 3.5003 | 6500 | 0.1507          |
+| 0.1762        | 3.7695 | 7000 | 0.1360          |
+| 0.168         | 4.0388 | 7500 | 0.1232          |
+| 0.1186        | 4.3080 | 8000 | 0.1193          |
+| 0.1227        | 4.5773 | 8500 | 0.1134          |
+| 0.1273        | 4.8465 | 9000 | 0.1089          |
+### Framework versions
+- Transformers 4.48.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

generation_config.json ADDED Viewed

	@@ -0,0 +1,9 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.48.2"
+}