Instructions to use jaweed123/Qwen3.5-0.8B-Python-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Transformers:

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

pipe = pipeline("text-generation", model="jaweed123/Qwen3.5-0.8B-Python-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaweed123/Qwen3.5-0.8B-Python-SFT")
model = AutoModelForCausalLM.from_pretrained("jaweed123/Qwen3.5-0.8B-Python-SFT", 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

llama.cpp

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT: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 jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT: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 jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Use Docker

docker model run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

LM Studio
Jan

vLLM

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaweed123/Qwen3.5-0.8B-Python-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaweed123/Qwen3.5-0.8B-Python-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

SGLang

How to use jaweed123/Qwen3.5-0.8B-Python-SFT 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 "jaweed123/Qwen3.5-0.8B-Python-SFT" \
    --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": "jaweed123/Qwen3.5-0.8B-Python-SFT",
		"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 "jaweed123/Qwen3.5-0.8B-Python-SFT" \
        --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": "jaweed123/Qwen3.5-0.8B-Python-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Ollama:
```
ollama run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
```

Unsloth Studio

How to use jaweed123/Qwen3.5-0.8B-Python-SFT 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 jaweed123/Qwen3.5-0.8B-Python-SFT 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 jaweed123/Qwen3.5-0.8B-Python-SFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jaweed123/Qwen3.5-0.8B-Python-SFT to start chatting

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Docker Model Runner:
```
docker model run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
```

Lemonade

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-Python-SFT-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.5-0.8B-Python-SFT / README.md

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-0.8B-Base
	tags:
	- qwen
	- python
	- code-generation
	- qlora
	- sft
	- unsloth
	- gguf
	datasets:
	- code-search-net/code_search_net
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Qwen3.5-0.8B-Python-SFT

	Python code generation model — Qwen3.5-0.8B-Base fine-tuned with **QLoRA
	(Supervised Fine-Tuning) on CodeSearchNet (Python)**: docstring → function
	code pairs.

	## Model Details

	\| Property \| Value \|
	\|----------\|-------\|
	\| Base model \| [Qwen/Qwen3.5-0.8B-Base](https://huggingface.co/Qwen/Qwen3.5-0.8B-Base) \|
	\| Method \| QLoRA (4-bit base + LoRA r=16, alpha=32) \|
	\| Trainable params \| 6.4M / 759M (0.84%) \|
	\| Dataset \| CodeSearchNet Python — 408K samples (13,590 repos) \|
	\| Task \| Docstring → Python function code \|
	\| Sequence length \| 2048 \|
	\| Precision \| BF16 \|
	\| Hardware \| NVIDIA RTX 4060 8GB \|

	## Training Results

	\| Metric \| Value \|
	\|--------\|-------\|
	\| Train loss \| 0.330 \|
	\| Eval loss \| 1.214 \|
	\| Steps \| 25,524 (1 epoch) \|
	\| Runtime \| ~25.7h \|

	## Evaluation

	pass@1 (temperature 0.2), official test harness, both models in bf16.

	\| Benchmark \| Base \| Fine-tuned \| Improvement \|
	\|-----------\|-----:\|---------------:\|------------:\|
	\| HumanEval \| 1.2% \| 17.7% \| 14.5x \|
	\| MBPP \| 0.0% \| 0.2% \| 0 → 1 \|

	Full report with example solutions: `reports/evaluation_report.md` in the training repo.

	## Training Details

	- Method: QLoRA — 4-bit quantized base + LoRA (r=16, alpha=32, dropout=0)
	- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
	- Optimizer: adamw_8bit (bitsandbytes), cosine schedule, 3% warmup
	- Batch: 2 per device × 8 grad accumulation (effective 16)
	- Max sequence length: 2048
	- Hardware: NVIDIA RTX 4060 8GB, ~25.7h
	- Data: CodeSearchNet Python filtered to ≤2048 tokens (408,377 train samples)

	## Usage

	### Transformers (LoRA adapter)

	```python
	from unsloth import FastLanguageModel

	model, tokenizer = FastLanguageModel.from_pretrained(
	"Qwen/Qwen3.5-0.8B-Base",
	max_seq_length=2048,
	load_in_4bit=True,
	)
	model, tokenizer = FastLanguageModel.from_pretrained(
	"jaweed123/Qwen3.5-0.8B-Python-SFT",
	max_seq_length=2048,
	load_in_4bit=True,
	)
	```

	### GGUF (ollama / llama.cpp / vLLM)

	```bash
	# llama.cpp
	llama-cli -m qwen3.5-0.8b-python-sft-q4_k_m.gguf -p "Write a Python function that..."

	# Ollama
	ollama create qwen3.5-python -f Modelfile
	```

	```dockerfile
	# Modelfile
	FROM qwen3.5-0.8b-python-sft-q4_k_m.gguf
	TEMPLATE "{{ if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	<\|im_start\|>assistant
	"
	```

	## Files

	\| File \| Description \|
	\|------\|-------------\|
	\| `adapter_model.safetensors` \| LoRA adapter (small, ~13MB) \|
	\| `model.safetensors` \| Merged 16-bit model \|
	\| `qwen3.5-0.8b-python-sft-q4_k_m.gguf` \| GGUF Q4_K_M (~0.5GB) \|
	\| `qwen3.5-0.8b-python-sft-q8_0.gguf` \| GGUF Q8_0 (~0.9GB) \|
	\| `qwen3.5-0.8b-python-sft-f16.gguf` \| GGUF F16 \|

	## Limitations

	- Fine-tuned for Python function generation from docstrings
	- Trained on 2019-era open-source code
	- 0.8B scale — limited reasoning; best for straightforward code tasks