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)

# pip install -U transformers accelerate
# 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=256)
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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 @earendil-works/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

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
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)

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)

# 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

# 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

Downloads last month: 184

Safetensors

Model size

0.8B params

Tensor type

F32

BF16

Model tree for jaweed123/Qwen3.5-0.8B-Python-SFT

Base model

Qwen/Qwen3.5-0.8B-Base

Quantized

(57)

this model

jaweed123
/

Qwen3.5-0.8B-Python-SFT

Qwen3.5-0.8B-Python-SFT

Model Details

Training Results

Evaluation

Training Details

Usage

Transformers (LoRA adapter)

GGUF (ollama / llama.cpp / vLLM)

Files

Limitations

Model tree for jaweed123/Qwen3.5-0.8B-Python-SFT

Dataset used to train jaweed123/Qwen3.5-0.8B-Python-SFT