Instructions to use mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct

SGLang

How to use mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct 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 "mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct" \
    --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": "mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct",
		"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 "mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct" \
        --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": "mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct
```

Typescript-QWen2.5-Coder-3B-Instruct / README.md

mrcuddle

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metadata

library_name: transformers
license: other
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
  - axolotl
  - generated_from_trainer
datasets:
  - mhhmm/typescript-instruct-20k
model-index:
  - name: Qwen2.5-Coder-3B-Instruct-TS
    results: []

See axolotl config

axolotl version: 0.6.0

# axolotl_config.yaml

# Model configuration
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
hub_model_id: mrcuddle/Qwen2.5-Coder-3B-Instruct-TS

# Training parameters
learning_rate: 0.0001  # Adjusted for potential stability improvement
train_batch_size: 4  # Increased for better gradient estimates
eval_batch_size: 4  # Increased for better evaluation stability
num_epochs: 1
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
gradient_accumulation_steps: 2
micro_batch_size: 1


# Distributed training settings
distributed_type: GPU
num_devices: 2  # Adjusted to utilize multiple GPUs if available
total_train_batch_size: 8  # Adjusted to match train_batch_size * num_devices * gradient_accumulation_steps
total_eval_batch_size: 8  # Adjusted to match eval_batch_size * num_devices * gradient_accumulation_steps

# Random seed for reproducibility
seed: 42

datasets:
  - path: mhhmm/typescript-instruct-20k
    type: alpaca
    field_instruction: instruction
    field_output: output
    format: "[INST] {instruction} [/INST]\n{output}"
    no_input_format: "[INST] {instruction} [/INST]"
    roles:
      input: ["USER"]
      output: ["ASSISTANT"]

Qwen2.5-Coder-3B-Instruct-TS

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-3B-Instruct on the mhhmm/typescript-instruct-20k dataset.

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: 0.0001
train_batch_size: 1
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 1

Training results

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0