Instructions to use Saif658/Saif-1-0-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saif658/Saif-1-0-Coder with Transformers:

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

pipe = pipeline("text-generation", model="Saif658/Saif-1-0-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Saif658/Saif-1-0-Coder")
model = AutoModelForCausalLM.from_pretrained("Saif658/Saif-1-0-Coder")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Saif658/Saif-1-0-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Saif658/Saif-1-0-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saif658/Saif-1-0-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saif658/Saif-1-0-Coder

SGLang

How to use Saif658/Saif-1-0-Coder 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 "Saif658/Saif-1-0-Coder" \
    --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": "Saif658/Saif-1-0-Coder",
		"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 "Saif658/Saif-1-0-Coder" \
        --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": "Saif658/Saif-1-0-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Saif658/Saif-1-0-Coder 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 Saif658/Saif-1-0-Coder 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 Saif658/Saif-1-0-Coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Saif658/Saif-1-0-Coder to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Saif658/Saif-1-0-Coder",
    max_seq_length=2048,
)

Docker Model Runner
How to use Saif658/Saif-1-0-Coder with Docker Model Runner:
```
docker model run hf.co/Saif658/Saif-1-0-Coder
```

Saif-1-0-Coder / README.md

Saif658

Update README.md

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	---
	base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	license: apache-2.0
	language:
	- en
	datasets:
	- Sharathhebbar24/Evol-Instruct-Code-80k-v1
	---
	# Saif-1.0-Coder

	A code-focused assistant fine-tuned from Llama 3.2 3B Instruct.

	## Model Details
	- Base model: unsloth/Llama-3.2-3B-Instruct
	- Fine-tuned by: Saif658
	- Training: QLoRA 4-bit, 500 steps
	- Dataset: Sharathhebbar24/Evol-Instruct-Code-80k-v1
	- License: Apache 2.0

	## What it's good at
	- Writing code in Python, JavaScript, Java, C, C++, C#, TypeScript, PHP, Go, Rust
	- Explaining code and algorithms
	- Debugging and fixing code

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

	tokenizer = AutoTokenizer.from_pretrained("Saif658/Saif-1.0-Coder")
	model = AutoModelForCausalLM.from_pretrained(
	"Saif658/Saif-1.0-Coder",
	torch_dtype=torch.float16,
	device_map="auto"
	)

	messages = [{"role": "user", "content": "Write a binary search in Python"}]
	inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
	outputs = model.generate(inputs, max_new_tokens=200)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Limitations
	Small 3B model — may struggle with very complex or long codebases.