typescript-slm-7b-reasoning-full/README.md

---
language:
- en
license: mit
library_name: transformers
tags:
- code
- typescript
- reasoning
- react
- nextjs
- angular
- nodejs
- deepseek
- gguf
- ollama
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
datasets:
- github-code
model-index:
- name: TypeScript-SLM-7B-Reasoning-Full
  results: []
---

# TypeScript-SLM-7B-Reasoning-Full

**TypeScript-SLM-7B-Reasoning** is a 7B-parameter DeepSeek-based model fine-tuned for step-by-step TypeScript reasoning. It merges the base model with LoRA adapters and includes GGUF quantization for local/Ollama workflows.

This repository hosts the **full merged model** plus **GGUF (q4_k_m)** for lightweight inference.

## Model Description

- **Base Model**: [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
- **Model Type**: Causal LM (code reasoning)
- **Parameters**: 7B
- **Context Length**: Inherits base DeepSeek-R1-Distill-Qwen-7B window
- **Fine-tuning**: LoRA on TypeScript reasoning/debugging tasks
- **License**: MIT
- **Language**: English, TypeScript/JavaScript code
- **System Prompt**: Focus on step-by-step debugging, refactoring, and design-level explanations before giving the final typed solution.

### What it is good at
- ✅ Explaining TypeScript bugs and fixes
- ✅ Refactoring and API design discussions
- ✅ Generating strongly-typed code for React/Next.js/Angular/Node.js
- ✅ Producing clear reasoning traces before final answers

## Intended Uses

**Primary**: TypeScript reasoning, debugging, refactoring, and guided code generation.  
**Out-of-scope**: Arbitrary natural-language chat unrelated to code; safety-sensitive or factual tasks outside TypeScript.

### Prompt Examples

```
"Debug this TypeScript function and explain the bug step by step:\n\nfunction add(a?: number, b?: number) { return a + b; }"

"Design a typed API surface for a Next.js todo service. Explain design choices, then show the final code."
```

## How to Use

### Ollama (recommended for local)

```bash
ollama create typescript-slm-7b-reasoning -f gguf/Modelfile-q4_k_m
ollama run typescript-slm-7b-reasoning "Explain why this React hook re-renders too often..."
```

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "sylvester-francis/typescript-slm-7b-reasoning-full",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("sylvester-francis/typescript-slm-7b-reasoning-full")

prompt = "Refactor this TypeScript service for better typing and error handling..."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.3,
    top_p=0.95,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### GGUF (llama.cpp)

```bash
huggingface-cli download sylvester-francis/typescript-slm-7b-reasoning-full \
  gguf/typescript-slm-7b-reasoning-q4_k_m.gguf --local-dir ./models

./llama-cli -m ./models/gguf/typescript-slm-7b-reasoning-q4_k_m.gguf \
  -p "Explain and fix this TypeScript type error..."
```

## Model Files

- `gguf/typescript-slm-7b-reasoning-q4_k_m.gguf` (≈4.7GB)
- `gguf/Modelfile-q4_k_m` (Ollama import)

## Training Data (summary)

- Curated TypeScript code from popular GitHub repos (React, Next.js, Angular, Node.js)
- StackOverflow Q&A focused on debugging and reasoning
- Filters for strong typing, framework best practices, and reasoning-rich examples

## Training Configuration (LoRA)

```yaml
Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Method: LoRA fine-tuning
Target Domains: TypeScript reasoning, debugging, refactoring
LoRA Rank / Alpha: tuned for stability and reasoning depth
Optimizer: AdamW
Max Sequence Length: inherits base model context window
```

## Evaluation

Qualitative checks on TypeScript debugging/refactoring prompts show:
- Clear reasoning steps before final code
- Strong type usage and framework-aware patterns
- Concise, actionable fixes

## Safety & Limitations

- May generate incorrect code or hallucinate APIs—review before production use.
- Not a security scanner; do not rely on it for vulnerability assessments.
- Avoid non-code or high-stakes factual tasks.

## License

MIT for the fine-tuned model; base model license and dataset terms also apply.

## Contact

- Maintainer: Sylvester Francis (`@sylvester-francis` on Hugging Face)
- Issues/feedback: open a discussion on the model repo