typescript-slm-7b-reasoning-full

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

Libraries

How to use RedneckBOT/typescript-slm-7b-reasoning-full with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedneckBOT/typescript-slm-7b-reasoning-full")
model = AutoModelForCausalLM.from_pretrained("RedneckBOT/typescript-slm-7b-reasoning-full")
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]:]))

llama-cpp-python

How to use RedneckBOT/typescript-slm-7b-reasoning-full with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RedneckBOT/typescript-slm-7b-reasoning-full",
	filename="gguf/typescript-slm-7b-reasoning-q4_k_m.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RedneckBOT/typescript-slm-7b-reasoning-full with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

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 RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

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 RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

Use Docker

docker model run hf.co/RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

LM Studio
Jan

vLLM

How to use RedneckBOT/typescript-slm-7b-reasoning-full with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

SGLang

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

Ollama
How to use RedneckBOT/typescript-slm-7b-reasoning-full with Ollama:
```
ollama run hf.co/RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M
```

Unsloth Studio new

How to use RedneckBOT/typescript-slm-7b-reasoning-full 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 RedneckBOT/typescript-slm-7b-reasoning-full 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 RedneckBOT/typescript-slm-7b-reasoning-full to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RedneckBOT/typescript-slm-7b-reasoning-full to start chatting

Docker Model Runner
How to use RedneckBOT/typescript-slm-7b-reasoning-full with Docker Model Runner:
```
docker model run hf.co/RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M
```

Lemonade

How to use RedneckBOT/typescript-slm-7b-reasoning-full with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedneckBOT/typescript-slm-7b-reasoning-full:Q4_K_M

Run and chat with the model

lemonade run user.typescript-slm-7b-reasoning-full-Q4_K_M

List all available models

lemonade list

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
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)

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

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)

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)

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

Downloads last month: 248

Safetensors

Model size

8B params

Tensor type

F16

Model tree for RedneckBOT/typescript-slm-7b-reasoning-full

Base model

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

Quantized

(172)

this model