Instructions to use sylvester-francis/typescript-slm-7b-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sylvester-francis/typescript-slm-7b-reasoning with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
model = PeftModel.from_pretrained(base_model, "sylvester-francis/typescript-slm-7b-reasoning")

Transformers

How to use sylvester-francis/typescript-slm-7b-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="sylvester-francis/typescript-slm-7b-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sylvester-francis/typescript-slm-7b-reasoning", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sylvester-francis/typescript-slm-7b-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sylvester-francis/typescript-slm-7b-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sylvester-francis/typescript-slm-7b-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sylvester-francis/typescript-slm-7b-reasoning

SGLang

How to use sylvester-francis/typescript-slm-7b-reasoning 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 "sylvester-francis/typescript-slm-7b-reasoning" \
    --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": "sylvester-francis/typescript-slm-7b-reasoning",
		"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 "sylvester-francis/typescript-slm-7b-reasoning" \
        --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": "sylvester-francis/typescript-slm-7b-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sylvester-francis/typescript-slm-7b-reasoning with Docker Model Runner:
```
docker model run hf.co/sylvester-francis/typescript-slm-7b-reasoning
```

TypeScript SLM 7B - Reasoning Variant

7B TypeScript model with reasoning capabilities for TypeScript code generation, optimized for React, Next.js, Angular, and Node.js.

Model Details

Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Model Size: 7B parameters
Training Method: LoRA (Low-Rank Adaptation)
Context Length: 2048 tokens
LoRA Rank: 64
Training Dataset: 5,000 high-quality TypeScript samples

Reasoning Capabilities

This variant includes chain-of-thought reasoning for better code understanding and generation.

Training Configuration

Batch Size: 2
Gradient Accumulation: 16
Effective Batch Size: 32
Learning Rate: 0.0001
Epochs: 3
Hardware: Google Colab A100 40GB

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "sylvester-francis/typescript-slm-7b-reasoning")

# Generate code
prompt = "Write a React component with TypeScript:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Repository

https://github.com/sylvester-francis/slm-typescript-model

Downloads last month: 22

Model tree for sylvester-francis/typescript-slm-7b-reasoning

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Adapter

(162)

this model